{"id":"W2964350699","doi":"10.1002/bit.27128","title":"Increasing productivity of <i>Spirulina platensis</i> in photobioreactors using artificial neural network modeling","year":2019,"lang":"en","type":"article","venue":"Biotechnology and Bioengineering","topic":"Algal biology and biofuel production","field":"Energy","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Central Food Technological Research Institute, Council of Scientific and Industrial Research; Grand Challenges Canada","keywords":"Spirulina (dietary supplement); Photobioreactor; Productivity; Biomass (ecology); Growth rate; Biology; Food science; Cyanobacteria; Animal science; Pulp and paper industry; Environmental science; Botany; Biotechnology; Chemistry; Mathematics; Ecology; Engineering; Economics; Raw material","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":[],"consensus_categories":[],"category_scores_codex":[0.0003546447,0.0001755126,0.0002965739,0.0001939582,0.00005250096,0.000006129367,0.00009057947,0.0005181674,0.000005586126],"category_scores_gemma":[0.00004113225,0.0001618346,0.00003831527,0.0003947595,0.0001159808,0.0001181243,0.00008788839,0.0003529101,0.000002581269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000321006,"about_ca_system_score_gemma":0.00001320162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005230448,"about_ca_topic_score_gemma":0.00006449814,"domain_scores_codex":[0.9989079,0.00003863908,0.0002938564,0.0003775899,0.00005404023,0.0003279805],"domain_scores_gemma":[0.9996341,0.00002613586,0.00007943725,0.0002108864,0.00002558522,0.00002385152],"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":[0.0001432654,0.00002731916,0.01503683,0.00004708359,0.00002547104,0.000003779343,0.00002310047,0.1505595,0.8283383,0.003031881,2.297494e-7,0.002763241],"study_design_scores_gemma":[0.0003141217,0.0001464231,0.002240761,0.0001011857,0.00003285495,0.000152964,0.0001090144,0.4699489,0.5254524,0.001021311,0.0001026055,0.0003774084],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979942,0.0006114086,0.0003785515,0.0001761344,0.0005015362,0.0001669979,0.000001914213,0.0001381287,0.00003112297],"genre_scores_gemma":[0.9984249,0.00003956719,0.001350654,0.00001307188,0.0001462126,0.000002156286,0.000006434087,0.00001490784,0.000002106211],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3193894,"threshold_uncertainty_score":0.6599423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01683915175111169,"score_gpt":0.2097114365512742,"score_spread":0.1928722848001626,"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."}}