{"id":"W4400837237","doi":"10.5376/gab.2024.15.0009","title":"Beyond Traditional Bioremediation: The Potential of Engineered SynComs in Tackling Complex Environmental Pollutants","year":2024,"lang":"en","type":"article","venue":"Genomics and Applied Biology","topic":"Microbial bioremediation and biosurfactants","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bioremediation; Pollutant; Biochemical engineering; Environmental chemistry; Environmental science; Chemistry; Engineering; Biology; Ecology; Contamination","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001451782,0.00008931355,0.0001042638,0.00003301987,0.00004416519,0.00001348726,0.00009779527,0.00007264992,0.0009533302],"category_scores_gemma":[0.000001701326,0.00006413239,0.00002624777,0.0000832752,0.0002333686,0.00001979742,0.0000682822,0.00008187204,0.00005214686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005432392,"about_ca_system_score_gemma":0.000008181172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007219684,"about_ca_topic_score_gemma":0.00001375425,"domain_scores_codex":[0.999384,0.00001543555,0.0002008225,0.0001964595,0.00006079766,0.0001424843],"domain_scores_gemma":[0.9998322,0.00003148795,0.00003650281,0.00006593805,6.364355e-7,0.00003331102],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00001189191,0.00002078615,0.0006664739,0.000003394257,0.000008641411,0.000001050467,0.0001393449,0.0001959138,0.9792077,0.001726772,0.00008659365,0.01793141],"study_design_scores_gemma":[0.001994794,0.0002229313,0.8317988,0.00001633842,0.00006064477,0.00007199335,0.0008020831,0.02693561,0.0966787,0.01924261,0.02133939,0.0008361045],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997647,0.0001290818,0.0001469281,0.0004228975,0.0001704807,0.0001504517,0.000476981,0.000008989334,0.0008471282],"genre_scores_gemma":[0.9990527,0.0002213007,0.0003577383,0.0001805844,0.00007904292,0.000002852508,0.00008628004,0.000005894064,0.00001363339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.882529,"threshold_uncertainty_score":0.9999599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009849827514997585,"score_gpt":0.1899565981835175,"score_spread":0.1801067706685199,"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."}}