{"id":"W2073702975","doi":"10.1111/j.1440-1770.2004.00248.x","title":"The management lessons learned from sediment remediation in the Detroit River – western Lake Erie watershed","year":2004,"lang":"en","type":"article","venue":"Lakes & Reservoirs Science Policy and Management for Sustainable Use","topic":"Toxic Organic Pollutants Impact","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Wayne State University","keywords":"Dredging; Environmental remediation; Sediment; Environmental science; Watershed; Remedial action; Wildlife; Sediment control; Environmental protection; Contamination; Hydrology (agriculture); Ecology; Oceanography; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001958251,0.0002394545,0.0001568314,0.0002036954,0.001312936,0.000776713,0.001332054,0.00004827097,0.00003837508],"category_scores_gemma":[0.0001980992,0.0001517245,0.00005296841,0.001291561,0.001054011,0.001483856,0.0009823644,0.0001316284,0.00003117295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000854245,"about_ca_system_score_gemma":0.0000510667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002134756,"about_ca_topic_score_gemma":0.01749626,"domain_scores_codex":[0.9968981,0.00007662067,0.000305629,0.0006427071,0.0007937641,0.001283169],"domain_scores_gemma":[0.9987689,0.0001363558,0.0001223262,0.0007980875,0.00001058365,0.0001637309],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001211168,0.001579576,0.02401226,0.0004933406,0.0004995686,0.001103355,0.07799847,0.02614361,0.002272131,0.5522521,0.008701362,0.3037331],"study_design_scores_gemma":[0.003180278,0.0002291779,0.3276693,0.00004957844,0.00009974462,0.00000507767,0.02129134,0.0005539062,0.0005901447,0.1525118,0.4932784,0.0005412783],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9338226,0.00005340244,0.001234432,0.05512911,0.000126532,0.003138822,0.00008544567,0.00008324654,0.006326376],"genre_scores_gemma":[0.9877396,0.0006250307,0.0009421938,0.00133147,0.00005671758,0.0001780547,0.000008037097,0.00002031178,0.009098588],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.484577,"threshold_uncertainty_score":0.9999872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02212018876925471,"score_gpt":0.2793734601478877,"score_spread":0.257253271378633,"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."}}