Microbial communities in the larval midgut of laboratory and field populations of cotton bollworm (<i>Helicoverpa armigera</i>)
Bibliographic record
Abstract
We compared the bacterial communities in the larval midgut of field and laboratory populations of a polyphagous pest, the cotton bollworm (Helicoverpa armigera), using denaturing gradient gel electrophoresis (DGGE) of amplified 16S rDNA sequences and 16S library sequence analysis. DGGE profiles and 16S rDNA library sequence analysis indicated similar patterns of midgut microbial community structure and diversity: specific bacterial types existed in both populations, and a more diverse microbial community was observed in caterpillars obtained from the field. The laboratory population harbored a rather simple gut microflora consisting mostly of phylotypes belonging to Enterococcus (84%). For the field population, phylotypes belonging to Enterococcus (28%) and Lactococcus (11%), as well as Flavobacterium (10%), Acinetobacter (19%), and Stenotrophomonas (10%) were dominant members. These results provided the first comprehensive description of the microbial diversity of the midgut of the important pest cotton bollworm and suggested that the environment and food supply might influence the diversity of the gut bacterial community.
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 | 0.000 | 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) | 0.000 | 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".