What determines transfer of carbon from plants to mycorrhizal fungi?
Bibliographic record
Abstract
Biological Market Models are common evolutionary frameworks to understand the maintenance of mutualism in mycorrhizas. 'Surplus C' hypotheses provide an alternative framework where stoichiometry and source-sink dynamics govern mycorrhizal function. A critical difference between these frameworks is whether carbon transfer from plants is regulated by nutrient transfer from fungi or through source-sink dynamics. In this review, we: provide a historical perspective; summarize studies that asked whether plants transfer more carbon to fungi that transfer more nutrients; conduct a meta-analysis to assess whether mycorrhizal plant growth suppressions are related to carbon transfer; and review literature on cellular mechanisms for carbon transfer. In sum, current knowledge does not indicate that carbon transfer from plants is directly regulated by nutrient delivery from fungi. Further, mycorrhizal plant growth responses were linked to nutrient uptake rather than carbon transfer. These findings are more consistent with 'Surplus C' hypotheses than Biological Market Models. However, we also identify research gaps, and future research may uncover a mechanism directly linking carbon and nutrient transfer. Until then, we urge caution when applying economic terminology to describe mycorrhizas. We present a synthesis of ideas, consider knowledge gaps, and suggest experiments to advance the field.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".