Quantification of breeding sites parameters in shaping bacterial communities in <i>Aedes aegypti</i> (Diptera: Culicidae)
Bibliographic record
Abstract
Abstract Studies have demonstrated the importance of breeding site, few had disentangled the role of microbiome, physico-chemical and biological factors of water as well as landuse on larval microbial communities and their recruitment in mosquito. A quantitative exploration of the interplay of multiple factors on mosquito microbiome was performed using a dataset obtained through a field survey undertaken in French Guiana. Two complementary hypotheses were tested (i) the most dissimilar larval microbiome structures in breeding sites displayed the most contrasting water properties and land-use, (ii) a higher specificity level of environmental parameters have an incidence on larval microbiome. Variance partitioning approach validated the two hypothesis by providing evidence that water bacterial community is a most significant driver shaping the structure of the bacteriome in mosquito than other environmental parameters from the breeding sites. However, land-use does not play such important role to explain variance. Our results consolidate and complement the knowledge shaping mosquito microbiota but also highlighted the large unknown in understanding the ecology of the recruitment into host.
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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".