{"id":"W4389478361","doi":"10.1021/acsnano.3c08077","title":"Cell-Targeted Metal-Phenolic Nanoalgaecide in Hydroponic Cultivation to Enhance Food Sustainability","year":2023,"lang":"en","type":"article","venue":"ACS Nano","topic":"Algal biology and biofuel production","field":"Energy","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"State Key Laboratory of Polymer Materials Engineering; National Key Research and Development Program of China; Chinese Academy of Agricultural Sciences; Sichuan University; Department of Science and Technology of Sichuan Province; National Natural Science Foundation of China","keywords":"Hydroponics; Sustainability; Photobioreactor; Population; Chlorella vulgaris; Environmental science; Biotechnology; Photosynthesis; Agriculture; Algal bloom; Biosafety; Nutrient; Biology; Algae; Agronomy; Ecology; Botany; Biofuel","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":[0.0004951498,0.0001685831,0.0002009731,0.0002252311,0.0001238336,0.00001251862,0.0001958588,0.0002602924,0.00007463878],"category_scores_gemma":[0.0005600714,0.0001524839,0.00005559931,0.001517929,0.00005791931,0.0001667377,0.0001055309,0.0002139855,0.0006761937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000246923,"about_ca_system_score_gemma":0.0000792256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006101819,"about_ca_topic_score_gemma":0.0004186456,"domain_scores_codex":[0.9984269,0.0001775239,0.000305263,0.0005085328,0.0001371432,0.000444612],"domain_scores_gemma":[0.9992827,0.00006643583,0.00007279852,0.0003915462,0.0001293597,0.00005711751],"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":[0.0001902873,0.0001732781,0.005939559,0.00008827573,0.00004165066,0.00001032668,0.001127547,0.001165063,0.9698797,0.003240901,0.002100528,0.01604284],"study_design_scores_gemma":[0.000263752,0.0002865285,0.02687049,0.00000907309,0.00001141054,0.000001805231,0.0003253445,0.0000375508,0.936058,0.01522539,0.02064021,0.0002704786],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955801,0.0002012398,0.00004657773,0.002110924,0.0003584833,0.0003602756,0.000004522959,0.0002387605,0.001099073],"genre_scores_gemma":[0.9960322,0.00003407713,0.00009113637,0.0002260412,0.0001249902,0.00009434742,0.00009898489,0.00001862964,0.003279637],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03382178,"threshold_uncertainty_score":0.8691327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01022180637342785,"score_gpt":0.2644063325783969,"score_spread":0.254184526204969,"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."}}