Unraveling the mystery of subsurface microorganisms in bioremediation
Bibliographic record
Abstract
Microorganisms are the key players in biogeochemical processes in the shallow subsurface for bioremediation. Estimation of genetic diversity, microbial activity, and their metabolic functions are the most applicable methods to explore the biodegradation processes in the subsurface. Several techniques such as community-level physiological profiling, sequencing, metagenomics, enzyme assay, and culture-based methods are used to investigate the metabolic potential, functional diversity, and genetic diversity of inherent microbial communities. These studies help to understand the metabolic pathways and biodegradation patterns in the subsurface, however, the interspecies microbial interactions and their transport mechanism in the porous media toward bioremediation of subsurface pollutants are still not well understood. Despite the advanced characterization methods for microbes, multiple crucial knowledge gaps of the dynamic subsurface microbial community remain unraveled. A detailed understanding of subsurface microbial interactions, mineral-metal-microbial correlation, transport mechanism, and degradation pathways will help in the optimization and design of in-situ bioremediation in the future. There is a need for detail-oriented exploration of these fundamental microbial processes to gain a greater understanding of microbial dynamics to facilitate the advanced bioremediation processes sustainably.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".