{"id":"W4230381596","doi":"10.26434/chemrxiv-2021-b36vm","title":"Ultrafast reclamation of fracking effluents using surface-engineered nanosilicon sponges","year":2021,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Surface Modification and Superhydrophobicity","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Fisheries and Oceans Canada; Canada Foundation for Innovation; National Natural Science Foundation of China; Imperial College London; University of Toronto; Suncor Energy Incorporated","keywords":"Land reclamation; Diluent; Effluent; Wastewater; Adsorption; Materials science; Environmental science; Chemical engineering; Waste management; Pulp and paper industry; Environmental engineering; Chemistry; Engineering; Nuclear chemistry; Organic 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005937714,0.0003152987,0.000574947,0.00008583163,0.00009263307,0.0001542543,0.000412283,0.0004047672,0.000621101],"category_scores_gemma":[0.0002149458,0.0003362752,0.0001767437,0.0002049045,0.0001019505,0.0001634631,0.0002592076,0.0003231362,0.00003472556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001512049,"about_ca_system_score_gemma":0.000183126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002191301,"about_ca_topic_score_gemma":0.000005996358,"domain_scores_codex":[0.997857,0.0001641929,0.0006214863,0.0006443077,0.0004296983,0.0002833359],"domain_scores_gemma":[0.9982995,0.0001015925,0.000363352,0.0008100177,0.0003328944,0.00009267375],"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":[0.00001392088,0.00007808681,0.002158248,0.0003463782,0.00001872689,0.000003240771,0.001022966,0.03221672,0.9640855,0.000007595872,0.00001028258,0.00003832234],"study_design_scores_gemma":[0.000215242,0.000006178217,0.003500151,0.0003948697,0.00004005979,0.00000366712,0.0002760527,0.01164978,0.9835662,0.00002511632,0.0000221105,0.000300619],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961225,0.0003747023,0.001532142,0.00005793371,0.001273046,0.0002973655,0.00001700783,0.0001226767,0.0002025595],"genre_scores_gemma":[0.9958831,0.00007915949,0.003750633,0.00002773216,0.00006539639,0.000008609925,0.00009339506,0.00004124171,0.00005073383],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02056694,"threshold_uncertainty_score":0.9999089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04776025632557861,"score_gpt":0.2772476703357586,"score_spread":0.22948741401018,"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."}}