{"id":"W4411067419","doi":"10.1007/s11157-025-09725-7","title":"Bioremediation meets biosensing: leveraging microbial electrochemical cell-based biosensors","year":2025,"lang":"en","type":"article","venue":"Reviews in Environmental Science and Bio/Technology","topic":"Microbial Fuel Cells and Bioremediation","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Biosensor; Bioremediation; Biochemical engineering; Nanotechnology; Microbial fuel cell; Computer science; Environmental science; Chemistry; Materials science; Engineering; Biology; Contamination; Ecology; Electrode","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008592187,0.0002326866,0.0002788692,0.0005082242,0.000241437,0.00003889561,0.0003795064,0.0002109476,0.0001215771],"category_scores_gemma":[0.00008672905,0.0001987396,0.00004664547,0.002205626,0.001661331,0.0001694719,0.0003286835,0.0002241387,0.0001436502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006334445,"about_ca_system_score_gemma":0.00003959814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001905725,"about_ca_topic_score_gemma":0.00002596395,"domain_scores_codex":[0.9979662,0.00004910373,0.0004266438,0.0007459496,0.000279419,0.0005327362],"domain_scores_gemma":[0.9994661,0.00002089621,0.0001433114,0.0002856929,0.000005103545,0.00007889758],"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":[0.000006083297,0.00009214687,0.0300095,0.00001813469,8.493424e-7,0.000002437368,0.00002312677,0.000005844512,0.9452106,0.00001277694,0.0002212006,0.02439724],"study_design_scores_gemma":[0.0003853186,0.00005434592,0.005926603,0.00006490631,0.00001337411,0.000006132146,0.00005235201,0.0005975665,0.9438093,0.0001343466,0.04870525,0.0002504937],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935601,0.002004599,0.000111852,0.003011469,0.000150067,0.0005281628,0.000002662984,0.00003969815,0.0005913263],"genre_scores_gemma":[0.9928361,0.003892141,0.002427523,0.0007343733,0.00001846385,0.00001313125,0.00000978351,0.000007349656,0.00006113774],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04848405,"threshold_uncertainty_score":0.8104364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005511236659126059,"score_gpt":0.2134309816868729,"score_spread":0.2079197450277469,"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."}}