Dara for: Functional traits of plant roots and Collembola determine their tri-trophic interactions with soil microbes
Bibliographic record
Abstract
Traditionally, leaf litter has been recognized as the main driver of the soil food web, but more recently roots have been shown to play an important role in fueling soil organisms. Root functional traits were shown to have direct effects on microbes and Nematoda, but many black boxes remain such as the effects of root traits on Collembola. Here, in a microcosm experiment, we studied the tri-trophic interactions between roots, microbes and Collembola in relation to ten plant species individually. Our results showed that plant species identity can drive variability in Collembola community structure, and this variability is best explained by root traits and microbial communities. Collembola feeding traits based on mandibular morphology were useful to identify top-down control on microbial communities. Our study also suggests that root traits such as fine root length and root diameter modify Collembola-microbe interactions, probably by modifying soil porosity. Overall, we obtained better results by looking at the whole system, rather than looking at bi-trophic interactions. This illustrates the importance of a holistic approach when studying biotic interactions in soil ecosystems. The data set included: - Collembola species abundance / microcosms - PLFA (phospholipid fatty acids) of microbes / microcosms - Functional traits value of plant roots - Functional trait value of Collembola - Soil chemical data
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.032 |
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".