Deciphering the relative contributions of multiple functions within plant–microbe symbioses
Bibliographic record
Abstract
For microbial symbioses with plants, such as mycorrhizas, we typically quantify either the net effects of one partner on another or a single function a symbiont provides. However, many microbial symbioses provide multiple functions to plants that vary based on the microbial species or functional group, plant species, and environment. Here we quantified the relative contributions of multiple functions provided by arbuscular mycorrhizal (AM) fungi to symbiont-mediated changes in plant biomass. We used two published data sets, one that measured multiple functions (pathogen protection and nutrient uptake) on a single plant and one that measured a single function (pathogen protection) on multiple plants. Using structural equation modeling, we observed strong variation in the functional pathways by which AM fungi altered plant growth; changes in plant biomass were associated with different functions (and different AM fungal functional groups) for the different plant species. Utilizing this methodology across multiple partners and environments will allow researchers to gauge the relative importance of functions they isolate and, perhaps more importantly, those they did not consider. This baseline information is essential for establishing the specific mechanisms by which microbial symbioses influence plant diversity and to more effectively utilize these organisms in agriculture, restoration and conservation.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".