ArticleScientific data2025
A soil temperature dataset based on random forest in the Three River Source Region.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Changes in soil temperature (ST) in the Three River Source Region (TRSR) significantly influence regional climate, ecology, and hydrological processes. However, existing models and reanalysis data exhibit considerable deviations in ST due to limitations in physical processes and parameterization schemes. To address this issue, we developed a new ST dataset using the Random Forest method (RFST), integrating observed ST data with relevant gridded datasets. RFST provides monthly ST data at nine layers with a spatial resolution of 0.01° × 0.01° from 1982 to 2015. Validation against two soil observation networks and six meteorological stations shows that the Nash-Sutcliffe Efficiency (NSE) of RFST exceeds 0.7 at all depths. Compared to ERA5 and CRA40, RFST corrects the cold bias, improves NSE, and reduces RMSE from 4 °C-8 °C to 1 °C-2 °C. RFST not only corrects the underestimation of ST and its warming rate but also aligns more closely with observed values for surface freezing and thawing indices as well as soil freeze-thaw periods, providing a more accurate representation of soil thermal conditions in the TRSR.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.