Fate of Nonindigenous, Endospore-Forming Bacteria in Soils. Strategies for Laboratory and Field Investigations
Bibliographic record
Abstract
Persistence of nonindigenous microorganisms released onto soils or into natural environments can have a significant impact on Department of Defense (DoD) operations. An understanding of competition among various microbial communities is necessary to accurately predict the types of microorganisms that will flourish as well as those that will wane under differing environmental scenarios. In the past, soil microbiology was altered with brute-force techniques such as the saturation of a soil with a decontamination agent. An approach that is more feasible for large areas is to alter soil conditions to promote the desired microbial status or to effectively predict their fate in field conditions. Ultimately, the ability to accurately predict the occurrence of a dominant microbial community will be useful both for predicting the fate of pathogens in the environment and for fostering success in the bioremediation of soils and sediments. Bacillus globigii (BO) was selected to investigate the persistence and fate of nonindigenous bacteria released onto soils. We were able to differentiate BO from indigenous bacteria by combining culturing techniques with lipid-based validation. Enrichment on agar plates produced bright orange BO colonies that were clearly distinct from native microorganisms. These data suggest that there is either an initial loss in viability or an inability to recover 10-15% of the BG soon after inoculation onto the soil.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".