{"id":"W4319963450","doi":"10.1016/j.algal.2023.103002","title":"Extraction of lipids from microalgal slurries with liquid CO2","year":2023,"lang":"en","type":"article","venue":"Algal Research","topic":"Algal biology and biofuel production","field":"Energy","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique; MacEwan University; Queen's University","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science; Canada Research Chairs","keywords":"Extraction (chemistry); Slurry; Yield (engineering); Supercritical fluid; Supercritical carbon dioxide; Supercritical fluid extraction; Pulp and paper industry; Mass transfer; Volumetric flow rate; Aqueous solution; Chemistry; Algae; Carbon dioxide; Chromatography; Environmental engineering; Environmental science; Materials science; Botany; Biology; Organic chemistry; Thermodynamics; Metallurgy","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":[0.0003368139,0.0007688224,0.0003704113,0.0005319174,0.0007053982,0.000495506,0.0002516149,0.0003042734,0.001124434],"category_scores_gemma":[0.0003922212,0.0002314233,0.0003168239,0.000656402,0.0002668516,0.000550693,0.0005302234,0.0007240291,0.0009246934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003451676,"about_ca_system_score_gemma":0.0006986706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004428542,"about_ca_topic_score_gemma":0.008015178,"domain_scores_codex":[0.9996979,0.00003005653,0.00003173046,0.00005955071,0.00008970536,0.00009101014],"domain_scores_gemma":[0.9998415,0.00003972219,0.00001631802,0.0000211861,0.0000568532,0.0000244371],"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":[0.000100587,0.00001555716,0.000294079,0.00002444639,0.000006569386,0.00007810425,0.00003473151,0.00003378673,0.9980566,0.00003135057,0.0000308055,0.001293445],"study_design_scores_gemma":[0.00001115566,0.0000864834,0.002432773,0.000009044978,0.00001061701,0.00007440604,0.0000658064,0.0003019566,0.9940397,0.00005550012,0.002906711,0.000005853961],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9684363,0.002027364,0.02006683,0.0003263619,0.0002002588,0.0002416837,0.001813392,0.0001372328,0.006750504],"genre_scores_gemma":[0.9496793,0.002462708,0.02367215,0.0002541991,0.00007521598,0.0001964052,0.007371891,0.000140691,0.01614759],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004428542,"threshold_uncertainty_score":0.008805513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06376238458678403,"score_gpt":0.358184105955717,"score_spread":0.294421721368933,"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."}}