CoBacFM: Core bacteria forecast model for global grassland pH dynamics under future climate warming scenarios
Bibliographic record
Abstract
Soil microbes regulate various biogeochemical cycles on Earth and respond rapidly to climate change, which is accompanied by changes in soil pH. However, the long-term patterns of these changes under future climate scenarios remain unclear. We propose a core-bacteria-forecast model (CoBacFM) to model soil pH changes by shifts of core bacterial groups under future scenarios using a curated soil microbiota dataset of global grasslands. Our model estimates that soil pH will increase in 63.8%–67.0% of grassland regions and decrease in 10.1%–12.4% of regions. Approximately 32.5%–32.9% of regions will become more alkaline by 5.6%, and these areas expand in all future scenarios. These results were supported by 14 warming simulation experiments. Using bacterial responses as bioindicators of soil pH, the CoBacFM method can accurately forecast pH changes in future scenarios, and the changing global climate is likely to result in the alkalization of grasslands.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".