{"id":"W6986486156","doi":"","title":"Predispersed solvent extraction of heavy metals using colloidal liquid aphrons","year":2017,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Extraction and Separation Processes","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Colloid; Heavy metals; Extraction (chemistry); Solvent extraction; Solvent; Metal","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000520669,0.0007135113,0.0008929622,0.0005079265,0.0009941071,0.0001333657,0.0004975356,0.000904389,0.0006667291],"category_scores_gemma":[0.0004272522,0.0008291762,0.0004602861,0.000300294,0.00006144704,0.001589109,0.00002905826,0.001138745,0.0001087806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005722804,"about_ca_system_score_gemma":0.0001089468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001479697,"about_ca_topic_score_gemma":0.0009886881,"domain_scores_codex":[0.9967912,0.0001160149,0.001148791,0.0006572537,0.0007589574,0.0005277402],"domain_scores_gemma":[0.9974912,0.0001362312,0.0008322038,0.0007474927,0.0005217171,0.0002711037],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004471659,0.0002046964,0.00000267811,0.001177461,0.0004980383,0.00002011776,0.00002342729,0.007304987,0.9800841,0.004033321,0.00003366678,0.006170387],"study_design_scores_gemma":[0.0007011436,0.0001662544,0.0001231175,0.0005215306,0.0004641595,0.00004525608,0.0004243398,0.001465238,0.9048451,0.0006829193,0.08960491,0.0009560944],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.820915,0.000846479,0.00001171688,0.000004863011,0.003861723,0.0007305192,0.0008663106,0.0005014223,0.1722619],"genre_scores_gemma":[0.9856692,0.000686973,0.0006163042,0.00002336883,0.00009859561,0.0001036805,0.0007411491,0.0002254294,0.01183528],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1647542,"threshold_uncertainty_score":0.9994159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02843842378749777,"score_gpt":0.2899068392134085,"score_spread":0.2614684154259108,"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."}}