{"id":"W2969322496","doi":"10.1007/s10661-019-7763-y","title":"Characterization and spatial distribution of organic-contaminated sediment derived from historical industrial effluents","year":2019,"lang":"en","type":"article","venue":"Environmental Monitoring and Assessment","topic":"Toxic Organic Pollutants Impact","field":"Environmental Science","cited_by":44,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sediment; Environmental science; Harbour; Environmental remediation; Effluent; Environmental chemistry; Contamination; Estuary; Pollution; Ecotoxicology; Environmental engineering; Oceanography; Ecology; Chemistry; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001261068,0.0001543651,0.0001488742,0.001407302,0.0003714488,0.0004883971,0.0001644106,0.000266606,0.0003742476],"category_scores_gemma":[0.0001946585,0.0001083165,0.0001622746,0.001123598,0.0002413702,0.0001604877,0.000219941,0.000100157,0.0001139043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002559522,"about_ca_system_score_gemma":0.0002266413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01048674,"about_ca_topic_score_gemma":0.01366471,"domain_scores_codex":[0.9998823,0.00000726878,0.00001345298,0.00004291377,0.00003019194,0.00002389402],"domain_scores_gemma":[0.999861,0.00001539094,0.00004448176,0.000008674006,0.0000560096,0.00001438714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003615292,0.0001209124,0.6494591,0.00009673993,0.00005629729,0.0005198831,0.0003868797,0.001563712,0.3272143,0.0001358451,0.00008525928,0.01999967],"study_design_scores_gemma":[0.000003155513,0.00006588177,0.9804878,0.000004325565,0.00003378781,0.0002558673,0.0002494587,0.0009145575,0.01709137,0.00002217584,0.0008675863,0.000004109933],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989416,0.00008564557,0.0003891652,0.000004799109,0.000001420685,0.000002703941,0.0001629282,0.000006153046,0.0004056059],"genre_scores_gemma":[0.9988008,0.0001023443,0.0003810389,0.000003759873,0.000002427797,0.000002365107,0.0002860641,0.00000244851,0.0004187739],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01048674,"threshold_uncertainty_score":0.02085143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009332907399318762,"score_gpt":0.2178602501423706,"score_spread":0.2085273427430518,"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."}}