Can we manage microbial systems to enhance carbon storage?
Bibliographic record
Abstract
Climate change is an urgent environmental issue with wide-ranging impacts on ecosystems and society. Microbes are instrumental in maintaining the balance between carbon (C) accumulation and loss in the biosphere, actively regulating greenhouse gas fluxes from vast reservoirs of organic C stored in soils, sediments and oceans. Heterotrophic microbes exhibit varying capacities to access, degrade and metabolise organic C-leading to variations in remineralisation and turnover rates. The present challenge lies in effectively translating this accumulated knowledge into strategies that effectively steer the fate of organic C towards prolonged sequestration. In this article, we discuss three ecological scenarios that offer potential avenues for shaping C turnover rates in the environment. Specifically, we explore the promotion of slow-cycling microbial byproducts, the facilitation of higher carbon use efficiency, and the influence of biotic interactions. The ability to harness and control these processes relies on the integration of ecological principles and management practices, combined with advances in economically viable technologies to effectively manage microbial systems in the environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 teacher head, 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".