SoilFutures-BR: Bias-Corrected Soil Temperature Projections for Brazil
Bibliographic record
Abstract
Soil temperature is a fundamental variable for the Earth system, yet it remains highly sensitive to climate change, particularly in tropical regions such as Brazil. Despite its importance, no bias-corrected soil temperature dataset based on the latest CMIP6 projections is currently available for the country. Climate projections are model-dependent and often exhibit systematic biases, which makes bias correction an essential step for subsequent applications. To address this gap, we present STEM-BR: Soil Temperature Under Climate Change for Brazil, a new gridded dataset of historical (1950–2014) and future (2015–2100) soil temperature derived from 15 CMIP6 General Circulation Models (GCMs) under the SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios. The dataset was generated through systematic regridding, bias correction, and statistical refinement, and provides monthly series at 0.25° × 0.25° resolution, including both raw and bias-corrected outputs. We applied the Quantile Delta Mapping (QDM) approach to correct biases in monthly time series of soil temperature at three depths (0.07, 0.28, and 1.00 m).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.005 | 0.005 |
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".