The 19th Annual International Conference on Soils, Sediments and Water: Abstract Book / [Co-Directors: Paul T. Kosteki, Eward J. Calabrese, Clifford Bruell, and Brian J. Rothschild]
Bibliographic record
Abstract
Environmental biotechnology encompasses a wide range of characterization, monitoring and control or remediation technologies that are based on biological processes.Recent breakthroughs in our understanding of biogeochemical processes are leading to exciting new and cost effective ways to monitor and manipulate the environment.Bioremediation has proven to be one of the most cost effective and environmentally sound remediation technologies available at sites where it will work.Though not a "new" technology, given that petroleum land farming is about 50 years old, there are a number of exciting and relevant technologies derived from molecular biology that have tremendous implications for the future of this branch of environmental biotechnology.This should not be all that surprising considering that microbes are the dominant life on earth (>10 30 cells and >10 17 g) and have had >3.7 billion years to evolve.Bioventing, biopiles, biofilters, bioreactors, biosparging, prepared beds, reactive barriers, and intrinsic bioremediation (natural attenuation) are all become widely used for bioremediation of soil, air, and groundwater.Emerging technologies in include, bioimmobilization, biocurtains, bioaugmentation, and treatment trains, especially as applied to mixed waste, metals, and radionuclides in the environment.Examples of various new techniques for biostimulation and bioaugmentation and their efficacy will be discussed.The possibilities for genetically modified organisms will be considered along with the reasons that they have not been used for bioremediation up until now.Monitoring techniques that inventory and monitor terminal electron acceptors and electron donors, enzyme probes that measure functional activity in the environment, functional genomic microarrays, phylogenetic microarrays, metabolomics, proteomics, and quantitative PCR are also being rapidly adapted for studies in environmental biotechnology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.141 | 0.072 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".