Bacterial and Fungal Biomass Responses to Feeding by Larval<i>Aedes triseriatus</i>(Diptera: Culicidae)
Bibliographic record
Abstract
We investigated the effect of different densities (0, 20, or 40) of developing larval Aedes triseriatus (Say) on bacterial abundance, bacterial productivity, and leaf fungal biomass in a microcosm experiment. Larvae in the low-density treatment developed normally, but larvae at the high density were significantly slower to develop. Both bacterial abundance (direct microscopic counts) and bacterial productivity (3H-leucine incorporation rates) on leaf material were significantly lower in the presence of larvae. Bacterial abundance in the water column did not change significantly with treatment, but bacterial productivity varied with time and declined significantly at both larval densities. Bacteria on the walls and bottom of the containers also were less abundant and significantly less productive in the presence of larvae. Aside from presence/absence effects, there was no clear evidence that larval impacts were density-dependent. Leaf-associated fungal biomass, as measured by ergosterol levels, varied with time but was not significantly affected by any treatment, suggesting most fungal tissue was incorporated in the leaf matrix and unavailable to larvae. Based upon estimated biomass accrual and respiration of larvae, it appears that bacterial biomass and production were insufficient to account for carbon demands of growing larvae. Because fungal biomass and leaf mass likely contributed little to gross larval demands, other carbon sources (e.g., protozoa and extracellular microbial components) were probably used by larvae. Although apparently insufficient for all larval carbon demands, bacterial and leaf fungal biomass may be adequate for other larval nutritional needs (i.e., nitrogen and essential lipids).
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) | 0.001 | 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".