{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000895977,0.0001074168,0.0001585309,0.0002210377,0.0002121082,0.00002055895,0.0002047585,0.0001934416,0.0007219177],"category_scores_gemma":[0.0002619749,0.00008172494,0.00004217589,0.0008098122,0.0004600199,0.0001521123,0.000108884,0.0004570036,0.001054849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004357455,"about_ca_system_score_gemma":0.0001085711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004094724,"about_ca_topic_score_gemma":0.000480282,"domain_scores_codex":[0.9982958,0.0002852731,0.0001933213,0.0003571556,0.000451371,0.0004170072],"domain_scores_gemma":[0.9988624,0.0003730316,0.00005041944,0.0003204188,0.0003243498,0.00006940107],"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":[0.003661158,0.0001109646,0.003881365,0.0000488493,0.000145556,0.00003311082,0.0005034858,0.00007814619,0.9628606,0.006358389,0.01030964,0.01200871],"study_design_scores_gemma":[0.0005780903,0.001690199,0.04162899,0.00006007798,0.0000179058,0.00001529372,0.0007797635,0.0001050694,0.8544607,0.002853946,0.09759022,0.0002197531],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9887702,0.0002323632,0.00002152475,0.0009840823,0.0002655242,0.0001356705,0.00002244453,0.0001206899,0.009447473],"genre_scores_gemma":[0.9956845,0.0001784045,0.0001403835,0.00001892579,0.0005666135,0.00002976068,0.0001964816,0.0000191511,0.003165765],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1083999,"threshold_uncertainty_score":0.999723,"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."}}