Safe Use of Replicating Bacteria in Biological Control
Bibliographic record
Abstract
Naturally occurring entomopathogenic bacteria provide an important resource for pest control. Greatest benefit will be obtained from the application of replicating bacteria which can establish in the host's environment and provide long term control. Bacteria developed for pest control are required to be safety tested and registered, yet bacteria are frequently introduced into the environment to enhance plant growth or aid soil processes without regulation. Why then, is the use of insect pathogenic bacteria treated differently? Augmentation of bacteria already present in the environment is unlikely to have any unwanted side effects as application is only changing the spatial and temporal distribution of the microbe and will have little long term effect on the total population. Users of the bacteria, however, will be exposed to the bacterium at a level higher than experienced naturally and potential adverse effects of this interaction should be addressed through Tier 1 safety testing. Non-target organisms should also be tested. If new organisms (exotic strains or modified bacteria) are to be introduced to the environment, their potential effects on the environment should be considered. The question of horizontal gene flow from applied bacteria also needs to be addressed. A better understanding of microbial ecology and Systems for tracking new strains and genes are essential to develop appropriate assessment procedures to ensure the safe utilisation of bacteria in biological control.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".