Effects of the invasive plant garlic mustard (<i>Alliaria petiolata</i>) on bacterial communities in a northern hardwood forest soil
Bibliographic record
Abstract
We compared the effects of the invasive plant Alliaria petiolata (garlic mustard) and 2 native plants on soil bacterial communities in a mature mesophytic forest. Soil samples were collected from plant patches containing either Alliaria or the native plants Allium tricoccum (wild leek) and Gallium triflorum (bedstraw). Since Alliaria litter contains secondary compounds that have reported antimicrobial properties, soil was collected outside the root zone of the plants but within the plant patches such that the soil would have been influenced by the litter of the respective plant species but not by plant roots. DNA was extracted from the soil samples and used to amplify the 16S rRNA gene region using bacterial specific primers. Terminal restriction fragment length polymorphism (TRFLP) profiles of each bacterial community were used to examine differences in bacterial communities among the plant species and between August and April sampling. Bacterial richness, evenness, and diversity were not significantly affected by plant species. Non-metric multidimensional scaling (NMS) suggested that differences existed between August and April sampling, but that plant species litter exerted a much weaker effect on soil bacterial communities. Soil physiochemical conditions were significantly correlated with soil bacterial communities and may underlie the observed seasonal changes in bacterial communities.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".