Model behavior of arbuscular mycorrhizal fungi: predicting soil carbon dynamics under climate change
Bibliographic record
Abstract
In this commentary, I advocate for more detailed incorporation of arbuscular mycorrhizal (AM) fungi in Earth system models, to improve our projections of global climate change. Current Earth system models display relatively low predictability of soil C stocks, which limit our ability to estimate future climate conditions. A more explicit incorporation of microbial mechanisms can increase the accuracy of ecosystem-scale models that inform the larger-scale Earth system models. Of the numerous microbial groups that can influence soil C dynamics, AM fungi are particularly tractable for integration in models. Arbuscular mycorrhizal fungi are globally abundant and perform critical roles in C cycling, such as augmentation of net primary productivity and soil C storage. Moreover, AM communities exhibit relatively low diversity within ecosystems, compared with other microbial groups. In addition, global datasets of AM ecology are available for use in model development. Thus, AM communities can be readily simulated in next-generation trait-based models that link microbial diversity to ecosystem function. Altogether, we are well-poised to incorporate the dynamics of individual AM taxa in ecosystem models, which can then be coupled to Earth system models. Hopefully, these efforts would advance our ability to predict and plan for future climate change.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".