{"id":"W2967700889","doi":"10.1007/10_2019_98","title":"Environmental Aspects of Biotechnology","year":2019,"lang":"en","type":"article","venue":"Advances in biochemical engineering, biotechnology","topic":"Microbial Metabolic Engineering and Bioproduction","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Scope (computer science); Environmental impact assessment; Natural resource economics; Non-renewable resource; Industrial biotechnology; Resource (disambiguation); Biotechnology; Business; Natural resource; Environmental planning; Environmental resource management; Engineering; Environmental science; Biochemical engineering; Economics; Ecology; Biology; Renewable energy; Computer science","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":[0.00008120356,0.0002083045,0.000267737,0.0002195579,0.000008010566,0.000002231872,0.0003037155,0.0006592814,0.00002506398],"category_scores_gemma":[0.00009218902,0.0002152084,0.00006828824,0.0002637137,0.0001485589,0.000007336792,0.0001569571,0.0002598811,0.00002053339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002718664,"about_ca_system_score_gemma":0.00001244762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003313039,"about_ca_topic_score_gemma":0.000001288826,"domain_scores_codex":[0.9988214,0.000007239209,0.0002866214,0.0004837445,0.00007887406,0.0003221178],"domain_scores_gemma":[0.999375,0.000004405887,0.00007644579,0.00050267,0.000007521034,0.00003396653],"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":[0.00002352374,0.00006177781,0.0002642967,0.00004837594,0.00001465331,0.00000131614,0.000003259764,0.001123886,0.9918188,0.003009436,0.00002413826,0.003606552],"study_design_scores_gemma":[0.0003586654,0.0001926448,0.0002394415,0.00002250314,0.000004415735,0.00003849497,0.00001213637,0.0001696989,0.922572,0.00006438195,0.07611297,0.0002126369],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9880294,0.009056689,0.001907151,0.0001620729,0.0003977556,0.0001853577,0.00001484393,0.00008137238,0.0001653735],"genre_scores_gemma":[0.9931874,0.003877541,0.002717619,0.00001974646,0.00007777724,0.00001190255,0.00003775901,0.00002699176,0.00004325396],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07608884,"threshold_uncertainty_score":0.8775942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.001296621827223711,"score_gpt":0.1804836296251235,"score_spread":0.1791870077978998,"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."}}