{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002184628,0.0007663844,0.001359918,0.0003678942,0.0001066777,0.00002889964,0.0007062855,0.0009931169,0.0007830686],"category_scores_gemma":[0.0003970538,0.0006635694,0.0003286549,0.0003176287,0.0006693012,0.0002297308,0.002357836,0.001211583,0.00008665811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00014993,"about_ca_system_score_gemma":0.0001700496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001818819,"about_ca_topic_score_gemma":0.002042692,"domain_scores_codex":[0.9942724,0.0007683489,0.001355577,0.002347454,0.0004967964,0.0007594151],"domain_scores_gemma":[0.9967892,0.00006601432,0.0007082862,0.001933928,0.0003356053,0.0001669387],"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":[0.0003537478,0.0006229087,0.1057943,0.0008189633,0.0004729103,0.00001951571,0.002855757,0.0003572474,0.8853188,0.0001940552,0.00002902889,0.003162722],"study_design_scores_gemma":[0.0009087106,0.00008997345,0.1026984,0.0002023252,0.0001377187,0.00003074552,0.0001474396,0.0001002946,0.8908576,0.002027972,0.002124219,0.0006745214],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9943205,0.0006277728,0.0000298378,0.0006529464,0.00127993,0.0007601368,0.000104202,0.0001187863,0.002105925],"genre_scores_gemma":[0.9974127,0.0003824884,0.00015947,0.00003272433,0.0004429747,0.00009811916,0.0006839221,0.00007592329,0.000711633],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005538814,"threshold_uncertainty_score":0.9995816,"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."}}