{"id":"W4386917263","doi":"10.1021/acsomega.3c04065","title":"Impacts of Proximity to Primary Source Areas on Concentrations of POPs at Global Sampling Stations Estimated from Land Cover Information","year":2023,"lang":"en","type":"article","venue":"ACS Omega","topic":"Toxic Organic Pollutants Impact","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Northern Contaminants Program; United Nations Environment Programme; Government of Canada","keywords":"Environmental science; Endosulfan; Geospatial analysis; Agricultural land; Land cover; Sampling (signal processing); Land use; Geography; Human settlement; Environmental resource management; Physical geography; Agriculture; Environmental protection; Cartography; Pesticide; Ecology; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002848643,0.0002441944,0.0001191156,0.0007145143,0.0001220911,0.0002953857,0.0001477171,0.00009888315,0.001447773],"category_scores_gemma":[0.0006738164,0.0001052885,0.0004083805,0.001070375,0.0002087115,0.0002194071,0.000506038,0.000125979,0.0001558383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000282816,"about_ca_system_score_gemma":0.0002243462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02279218,"about_ca_topic_score_gemma":0.03376766,"domain_scores_codex":[0.9997774,0.00003857864,0.00001320738,0.00007453864,0.00005395111,0.00004225087],"domain_scores_gemma":[0.9996647,0.00009560485,0.0001249061,0.00003420176,0.00005538717,0.00002517819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007318143,0.00001231909,0.9786355,0.00003810786,0.00009910487,0.00008046534,0.0000870784,0.006479665,0.002542464,0.0001221188,0.000444803,0.0113852],"study_design_scores_gemma":[0.000001775646,0.00001921046,0.9947367,0.000003324071,0.00003377098,0.0000265633,0.0001040544,0.003929372,0.0006290902,0.00004086309,0.0004728715,0.000002417167],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963097,0.00005258375,0.0006547868,0.00001603237,0.000002945022,0.000007523067,0.002072966,0.00002263884,0.0008607211],"genre_scores_gemma":[0.9964268,0.00005112911,0.0004918232,0.000005442891,0.000002575545,0.000008668119,0.002720493,0.000004257357,0.000288892],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02279218,"threshold_uncertainty_score":0.04531902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01956536563891632,"score_gpt":0.2703028189008331,"score_spread":0.2507374532619168,"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."}}