{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003523872,0.0005096107,0.000575864,0.001989124,0.0002673604,0.001972541,0.0003771533,0.0009718154,0.003304592],"category_scores_gemma":[0.01493696,0.0002175851,0.0004071025,0.002345941,0.0009899989,0.002046141,0.001015629,0.0009388452,0.0001934384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001582719,"about_ca_system_score_gemma":0.0009881905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002266948,"about_ca_topic_score_gemma":0.002304394,"domain_scores_codex":[0.9984785,0.001029629,0.0000620097,0.00007322244,0.0002245561,0.0001320244],"domain_scores_gemma":[0.9861284,0.0113596,0.001512036,0.0001926693,0.0005106763,0.0002966989],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001901672,0.0009492408,0.1640326,0.001522882,0.00099188,0.0007408606,0.0003655976,0.425745,0.003259548,0.1509405,0.003517113,0.2460331],"study_design_scores_gemma":[0.0001812282,0.001938937,0.1640653,0.0005206473,0.0007474679,0.0003496757,0.00299727,0.5337543,0.005323954,0.2788678,0.0110925,0.0001609677],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9486211,0.004444166,0.02569403,0.002089577,0.00004335761,0.0001979537,0.000648581,0.00002378163,0.01823756],"genre_scores_gemma":[0.9957874,0.001252917,0.001970812,0.00002539317,0.00001962943,0.00002544859,0.0001157884,0.000002484029,0.0008000319],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003523872,"threshold_uncertainty_score":0.01863623,"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."}}