{"id":"W4281797028","doi":"10.1016/j.jhazmat.2022.129340","title":"Development of advanced oil/water separation technologies to enhance the effectiveness of mechanical oil recovery operations at sea: Potential and challenges","year":2022,"lang":"en","type":"article","venue":"Journal of Hazardous Materials","topic":"Oil Spill Detection and Mitigation","field":"Environmental Science","cited_by":110,"is_retracted":false,"has_abstract":false,"ca_institutions":"Fisheries and Oceans Canada; Dalhousie University; University of Northern British Columbia; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Fisheries and Oceans Canada; Canada Foundation for Innovation; Dairy Farmers of Ontario","keywords":"Decantation; Oil spill; Environmental science; Water injection (oil production); Waste management; Oil storage; Petroleum industry; Petroleum engineering; Environmental engineering; Engineering; Chemistry","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":[0.001060231,0.0004304982,0.0003232019,0.0003848124,0.0002809498,0.001457185,0.0006419506,0.0009270161,0.003410527],"category_scores_gemma":[0.0005168582,0.0001310309,0.0003991124,0.000371966,0.000596892,0.002045023,0.0006234727,0.001018896,0.0008162576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006249279,"about_ca_system_score_gemma":0.001936503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00114829,"about_ca_topic_score_gemma":0.002321626,"domain_scores_codex":[0.9997285,0.00004420911,0.00001271186,0.00004443228,0.0001173203,0.00005279605],"domain_scores_gemma":[0.9996837,0.00006262306,0.00006160951,0.00001751289,0.0001283939,0.00004614256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003417189,0.0007813189,0.003020383,0.001831673,0.00007136263,0.0002751761,0.0001069157,0.01082751,0.5320486,0.02903867,0.003745019,0.4179116],"study_design_scores_gemma":[0.0001272287,0.004129958,0.005903301,0.0004782191,0.0001064825,0.0005932793,0.001092545,0.03995468,0.7433422,0.01654576,0.1876532,0.00007321708],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"review","genre_scores_codex":[0.6791518,0.1296943,0.1246017,0.02038311,0.00105936,0.0002615465,0.000621189,0.0004575935,0.0437693],"genre_scores_gemma":[0.8283755,0.07558934,0.08452306,0.001115968,0.0002697418,0.00006527192,0.0004263893,0.00003346734,0.00960127],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.003410527,"threshold_uncertainty_score":0.01140934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008758134065272538,"score_gpt":0.2444022686291594,"score_spread":0.2356441345638869,"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."}}