{"id":"W3125176014","doi":"10.20944/preprints202101.0419.v1","title":"Biomass and Lipid Productivity by Two Algal Strains of &lt;em&gt;Chlorella Sorokiniana&lt;/em&gt; Grown in Hydrolysate of Water Hyacinth","year":2021,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Algal biology and biofuel production","field":"Energy","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of British Columbia; Indian Institute of Technology Roorkee; Department of Biotechnology, Ministry of Science and Technology, India","keywords":"Chlorella sorokiniana; Hydrolysate; Biomass (ecology); Food science; Photobioreactor; Chemistry; Nutrient; Total inorganic carbon; Algae; Carbon fibers; Carbohydrate; Productivity; Chlorella; Botany; Biology; Hydrolysis; Biochemistry; Carbon dioxide; Agronomy; Organic chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002233855,0.0007827906,0.0004512955,0.0003903896,0.0001457904,0.0004202768,0.0003412248,0.0003786857,0.0004670689],"category_scores_gemma":[0.0002580569,0.0003109501,0.0003362292,0.0004412815,0.0002349816,0.0002993616,0.0005695853,0.0004978037,0.0002558564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003609784,"about_ca_system_score_gemma":0.0002396809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00242517,"about_ca_topic_score_gemma":0.002828511,"domain_scores_codex":[0.9996759,0.00004117866,0.00005132294,0.00009061054,0.00008660288,0.00005429842],"domain_scores_gemma":[0.9996933,0.00005276621,0.00007601153,0.0000322834,0.0000611773,0.00008454497],"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":[0.0001241809,0.0000273653,0.0004972165,0.00002309445,0.000004199022,0.00002117365,0.00001856185,0.00002795132,0.9988148,0.00001092725,0.000006565524,0.0004240337],"study_design_scores_gemma":[0.00003694606,0.001530922,0.0563278,0.00001571511,0.00006136412,0.0002695126,0.0002422493,0.0008985886,0.9395398,0.00003041191,0.001021686,0.00002493638],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987128,0.0001888233,0.0002846679,0.00003791282,0.00000927264,0.00001188324,0.0003703174,0.00001520486,0.000369154],"genre_scores_gemma":[0.9916928,0.0003709671,0.002811942,0.00004515419,0.000008798994,0.00005965778,0.002483265,0.00002646636,0.00250113],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00242517,"threshold_uncertainty_score":0.004822135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0363504404548772,"score_gpt":0.2743013041704077,"score_spread":0.2379508637155305,"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."}}