Collembola diet-switching in the presence of maize roots vary with species
Bibliographic record
Abstract
Collembola are known to feed on a wide range of soil material, predominantly rhizosphere fungi, and root-derived substances. However, diet switching from these usual food sources to living roots (herbivory) was previously demonstrated for one species of collembola, Protaphorura fimata, a euedaphic species. The objective of this study was to determine if diet switching can be applied to another collembola species, Folsomia candida Willem. This hemiedaphic species was given different combinations of maize plants (−13.28‰ δ13C, 3‰ δ15N) and 15N-enriched rye grass litter (−28.88‰ δ13C, 17 516.86‰ δ15N) in a C3 soil system (−27.27‰ δ13C, 5.27‰ δ15N) under controlled conditions. After 8 wk, there was clear evidence of root feeding because the δ13C signature in collembola tissue was −19.28‰ in the presence of maize plants alone and −18.29‰ with maize plants grown in soil mixed with ryegrass litter, whereas collembola in unplanted soil microcosms had a δ13C signature of −23.66‰. Data analysis with a two-source isotope mixing model indicates that up to 60% of the carbon requirements of F. candida were derived from living maize roots. Whether collembola root feeding is due to grazing on roots directly or on mycorrhiza (root-fungus association) requires further investigation.
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".