{"id":"W142426465","doi":"10.2166/wqrj.2005.043","title":"Analyzing the Spatial Distribution of Sediment Contamination in the Lower Great Lakes","year":2005,"lang":"en","type":"article","venue":"Water Quality Research Journal","topic":"Heavy metals in environment","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; Toronto Metropolitan University","funders":"","keywords":"Environmental science; Hexachlorobenzene; Sediment; Contamination; Spatial distribution; Kriging; Mercury (programming language); Hydrology (agriculture); Environmental chemistry; Pollutant; Geology; Ecology; Remote sensing; Geomorphology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0199218,0.00008453519,0.000119743,0.0000386937,0.0003341839,0.0000774539,0.0004891664,0.00004595766,0.001610001],"category_scores_gemma":[0.0002075409,0.0000384011,0.00008404307,0.0001628286,0.0004990457,0.0002383892,0.00019204,0.0006155366,0.000187496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000590381,"about_ca_system_score_gemma":0.00001160298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005290185,"about_ca_topic_score_gemma":0.0005184594,"domain_scores_codex":[0.994846,0.002644402,0.0004689512,0.0001591847,0.001445346,0.0004361506],"domain_scores_gemma":[0.9992827,0.0002591261,0.00007533246,0.0002933161,0.0000217922,0.00006773977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005563902,0.002876186,0.4439636,0.00004356004,0.000134116,0.00008033455,0.03186102,0.01601482,0.1551578,0.0007241264,0.009458606,0.3391294],"study_design_scores_gemma":[0.000702588,0.0002722605,0.9048474,0.0000303521,0.00001369145,0.00004104274,0.001192618,0.001183227,0.03925539,0.00124274,0.05107646,0.000142186],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9867368,0.00004328541,0.002189912,0.01011499,0.00005765967,0.0002373422,0.000006012952,0.000002503253,0.0006114803],"genre_scores_gemma":[0.9994158,0.00006371789,0.00005765857,0.00006649316,0.0001498479,0.0000144585,0.000009340795,0.00000479385,0.0002178791],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4608839,"threshold_uncertainty_score":0.9993027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07169190734948326,"score_gpt":0.373256515962084,"score_spread":0.3015646086126007,"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."}}