{"id":"W2013132139","doi":"10.4028/www.scientific.net/amr.666.33","title":"Effect of Synthetic Wastewater by Electrochemical Pretreatment on <i>Chlorella vulgaris</i> Growth and Nutrients Removal","year":2013,"lang":"en","type":"article","venue":"Advanced materials research","topic":"Algal biology and biofuel production","field":"Energy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Fundamental Research Funds for the Central Universities; Chinese Universities Scientific Fund","keywords":"Chlorella vulgaris; Wastewater; Electrolysis; Biomass (ecology); Nutrient; Sewage treatment; Chemistry; Chlorella; Pulp and paper industry; Food science; Biology; Botany; Algae; Environmental engineering; Environmental science; Agronomy; Organic chemistry; Electrode","routes":{"ca_aff":true,"ca_fund":false,"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":[0.000778573,0.0002583835,0.0004116582,0.0001297014,0.0001590189,0.00004203584,0.0002073234,0.0002603612,0.000390583],"category_scores_gemma":[0.0004344466,0.0001777186,0.00004467501,0.0001726818,0.0003781586,0.000132449,0.0001463949,0.0001886472,0.0002538446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009444769,"about_ca_system_score_gemma":0.00001677438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006229318,"about_ca_topic_score_gemma":0.000001989965,"domain_scores_codex":[0.9971878,0.0007739746,0.0003399345,0.0006252088,0.0004059259,0.0006671337],"domain_scores_gemma":[0.9988771,0.0003762967,0.00008413544,0.0003897573,0.0001487375,0.0001239442],"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":[0.002090145,0.0001388867,0.00003243817,0.0001988036,0.00005606492,0.000006209219,0.00003666201,0.000001079935,0.9927176,0.00104632,0.000409892,0.003265865],"study_design_scores_gemma":[0.0009764597,0.002743513,0.00007301268,0.00006866251,0.00001722079,0.00002598381,0.000003670434,0.000003656541,0.9905922,0.002819678,0.002512163,0.0001638078],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965428,0.0006010539,0.000003620001,0.0002497734,0.0002038618,0.0008367884,0.00001699551,0.00004851165,0.001496627],"genre_scores_gemma":[0.9975658,0.000747107,0.0001557012,0.00001282722,0.0001163566,0.000250598,0.0001108639,0.00003443329,0.00100632],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003102057,"threshold_uncertainty_score":0.7247155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008665748161225456,"score_gpt":0.2717469702328099,"score_spread":0.2630812220715845,"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."}}