{"id":"W1967859728","doi":"10.1016/j.marpolbul.2012.07.023","title":"Assessing, managing and monitoring contaminated aquatic sediments","year":2012,"lang":"en","type":"article","venue":"Marine Pollution Bulletin","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"Fisheries and Oceans Canada; Golder Associates (Canada)","funders":"","keywords":"Prioritization; Environmental planning; Aquatic environment; Risk analysis (engineering); Government (linguistics); Environmental science; Environmental resource management; Computer science; Contamination; Business; Process management; Ecology; 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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006212774,0.0001411321,0.0001218317,0.00004251016,0.0002111993,0.00008676696,0.00008602622,0.00005165941,0.003948155],"category_scores_gemma":[0.00002435635,0.0001365221,0.00002757803,0.0001143786,0.00009622386,0.0002727951,0.0003271923,0.0001193624,0.00107439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002027096,"about_ca_system_score_gemma":0.000003520852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001304399,"about_ca_topic_score_gemma":0.00000784553,"domain_scores_codex":[0.9987972,0.0001468777,0.0002201422,0.0001934334,0.0002598771,0.0003824849],"domain_scores_gemma":[0.9995609,0.00002571993,0.00009097296,0.0001486435,0.000003118729,0.000170647],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001592484,0.0002009044,0.9335137,0.00002342295,0.00002605421,0.000004755634,0.0007870398,0.0000548186,0.003357078,0.0006035206,0.003896711,0.05751606],"study_design_scores_gemma":[0.0004635928,0.00002378035,0.8792172,0.00001933722,0.00002250275,0.00001596633,0.000258175,0.0003355689,0.001957892,0.000195912,0.1172836,0.0002064991],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9658656,0.00006943492,0.001973987,0.005487361,0.0004888193,0.0001872951,0.00000191327,0.0001062835,0.02581931],"genre_scores_gemma":[0.9922207,0.00001831123,0.003103181,0.0004536523,0.0001110546,0.00001541674,0.000007195958,0.00001198216,0.004058533],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1133869,"threshold_uncertainty_score":0.9997034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02417648407215038,"score_gpt":0.2804817178898201,"score_spread":0.2563052338176697,"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."}}