Mycorrhizas in changing ecosystems<sup>,</sup>
Bibliographic record
Abstract
Ecosystems change between arbuscular mycorrhizal and ectomycorrhizal vegetation dominance over anthropological and geological time scales, yet consequences for ecosystem function are unclear. We review four hypotheses for the effect of mycorrhizal status on ecosystem function. Specifically, that differences between ectomycorrhizal and arbuscular mycorrhizal dominated ecosystems are driven by (1) foliar trait differences, (2) positive plant–soil feedback in ectomycorrhizal plants, (3) differences in the ability to dissolve rocks as a source of nutrition, and (4) differences in the ability to use organic nutrients. We find no universal difference in foliar traits with mycorrhizal status. A spatial simulation suggests that positive plant–soil feedback in ectomycorrhizal plants is unlikely to drive ecosystem differences. However, negative feedback appears to be more common in arbuscular mycorrhizal trees than ectomycorrhizal trees and may represent an important ecosystem difference. Rock dissolution occurs under both mycorrhizal types but may differ in rate. Hypothesis 4 was the best supported: a model and some field evidence suggest that decoupling of carbon and nutrients in ectomycorrhizal decomposition leads to inhibition of saprotrophic mineralization, with context-dependent effects. Greater understanding of organic nutrient utilization differences may be key to improving incorporation of mycorrhizas in ecosystem ecology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".