{"id":"W2419683475","doi":"10.1021/acs.est.6b00732","title":"Microalgae Recovery from Water for Biofuel Production Using CO<sub>2</sub>-Switchable Crystalline Nanocellulose","year":2016,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Electrohydrodynamics and Fluid Dynamics","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science; Canada Research Chairs","keywords":"Nanocellulose; Biofuel; Sparging; Reusability; Chemical engineering; Cellulose; Biomass (ecology); Materials science; Pulp and paper industry; Dissolved air flotation; Chlorella vulgaris; Dispersion (optics); Nanotechnology; Chemistry; Environmental science; Waste management; Wastewater; Environmental engineering; Organic chemistry; Algae; Botany; Computer science; Agronomy","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":[0.00007480819,0.0004104283,0.0002488512,0.0002073399,0.0002285949,0.0003149,0.0001645519,0.0002418362,0.0009371276],"category_scores_gemma":[0.0001043809,0.0001320588,0.0002733346,0.0002738913,0.0001535346,0.0004404971,0.0003934707,0.0004157355,0.000446983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003492777,"about_ca_system_score_gemma":0.0003051959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00171305,"about_ca_topic_score_gemma":0.005062397,"domain_scores_codex":[0.9999206,0.000003662559,0.000004956425,0.00002274855,0.00003312219,0.00001487769],"domain_scores_gemma":[0.9999671,0.000004614562,0.000009040304,0.000003957436,0.000008842363,0.00000640473],"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":[0.00002035713,0.000006690324,0.0001458359,0.00003795695,0.000003378039,0.00004192629,0.000008006115,0.0001928718,0.9958497,0.00006525101,0.00004606622,0.003582044],"study_design_scores_gemma":[0.000002504061,0.00002373031,0.0008112668,0.000006186409,0.000006806154,0.00003630244,0.00002510511,0.001548745,0.9955617,0.00006109582,0.001912,0.000004534783],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9591122,0.003138124,0.02998453,0.0002378212,0.00008333863,0.00006073459,0.0003242838,0.0003316327,0.006727376],"genre_scores_gemma":[0.9757988,0.001827052,0.01795201,0.0001319582,0.00001475015,0.00004456047,0.0002552082,0.00007914069,0.003896506],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00171305,"threshold_uncertainty_score":0.003406107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004235052088362177,"score_gpt":0.1774740092897833,"score_spread":0.1732389572014212,"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."}}