{"id":"W4376139331","doi":"10.1016/j.biortech.2023.129162","title":"A reliable multi-nutrient model for the rapid production of high-density microalgal biomass over a broad spectrum of mixotrophic conditions","year":2023,"lang":"en","type":"article","venue":"Bioresource Technology","topic":"Algal biology and biofuel production","field":"Energy","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Ministère de l'Agriculture, des Pêcheries et de l'Alimentation; Université du Québec à Rimouski","funders":"Natural Sciences and Engineering Research Council of Canada; Institut sur la Nutrition et les Aliments Fonctionnels","keywords":"Mixotroph; Biomass (ecology); Nutrient; Environmental science; Range (aeronautics); Production (economics); Biological system; Pulp and paper industry; Biology; Mathematics; Heterotroph; Ecology; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0003009273,0.0001757179,0.0003022779,0.0004468489,0.0002304274,0.00000387409,0.0002944448,0.0004492582,0.00002803169],"category_scores_gemma":[0.0002407472,0.0001305031,0.0001280143,0.001054135,0.0008874135,0.00003779477,0.0001391447,0.0001937265,0.00002427507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004713884,"about_ca_system_score_gemma":0.00004133407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006053268,"about_ca_topic_score_gemma":0.0001494098,"domain_scores_codex":[0.9986792,0.00003646679,0.0003776708,0.0004534468,0.0001096156,0.0003436553],"domain_scores_gemma":[0.9988691,0.00005348895,0.0002778461,0.0006315747,0.0001441066,0.00002386819],"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":[0.0004288135,0.0002785192,0.001582565,0.0001149309,0.0001940648,0.000001828794,0.000155632,0.001817778,0.9675409,0.0210239,0.004671548,0.002189588],"study_design_scores_gemma":[0.001176629,0.0004007255,0.01235041,0.00003213362,0.0001370811,0.00003105875,0.000222465,0.01647199,0.9390443,0.02349967,0.006396038,0.0002375235],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898031,0.0004710657,0.001952115,0.006183076,0.0004618518,0.0006329733,0.00008145707,0.0004019134,0.00001246732],"genre_scores_gemma":[0.997739,0.0001317199,0.0009991828,0.00002688826,0.0001187762,0.0001203956,0.00009726197,0.00002264284,0.0007441397],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02849655,"threshold_uncertainty_score":0.5321761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01870772419337326,"score_gpt":0.2435149812146199,"score_spread":0.2248072570212467,"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."}}