{"id":"W2104335476","doi":"10.2166/wqrj.2001.025","title":"Identifying and Assessing the Economic Benefits of Contaminated Aquatic Sediment Cleanup","year":2001,"lang":"en","type":"article","venue":"Water Quality Research Journal","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; University of Toronto","funders":"","keywords":"Environmental remediation; Sediment; Environmental science; Valuation (finance); Aquatic ecosystem; Environmental planning; Contamination; Environmental resource management; Ecology; Business; Geology; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01043394,0.00009548589,0.0002669481,0.0001889016,0.0004167983,0.0003093881,0.0002285393,0.00005900397,0.0006649941],"category_scores_gemma":[0.00006558537,0.00007223683,0.00008696019,0.00005284254,0.0002125602,0.0005273954,0.0001425969,0.0003680316,0.0003678388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004126472,"about_ca_system_score_gemma":0.00002086884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000582833,"about_ca_topic_score_gemma":0.00003558561,"domain_scores_codex":[0.9981901,0.0002475009,0.0008568884,0.0002291832,0.00009921227,0.0003771293],"domain_scores_gemma":[0.9992401,0.0001614149,0.0002677161,0.0002097361,0.0000205064,0.0001005249],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004193995,0.0001069513,0.9513928,0.00004896,0.0002170098,0.000005407731,0.005563799,0.0007818046,0.001407209,0.02527094,0.0003867323,0.01477642],"study_design_scores_gemma":[0.001380285,0.000135993,0.9324929,0.00007090459,0.00001207496,0.00009605043,0.003901388,0.003828617,0.003101925,0.05248037,0.002253332,0.000246236],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9946793,0.001135511,0.0004722837,0.001409003,0.000176378,0.0001503057,0.000009488886,0.000004503107,0.001963177],"genre_scores_gemma":[0.9980249,0.001085248,0.0001278268,0.00005828713,0.0001191102,0.000006925456,0.000006989567,0.00001311917,0.0005575484],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02720943,"threshold_uncertainty_score":0.7281219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4149963611312306,"score_gpt":0.3931417148719476,"score_spread":0.02185464625928296,"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."}}