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Record W2786495144 · doi:10.3390/land7010011

Effect of Land Use Change on Soil Carbon Storage over the Last 40 Years in the Shi Yang River Basin, China

2018· article· en· W2786495144 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLand · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsMcGill University
FundersNational Natural Science Foundation of China
KeywordsEnvironmental scienceWoodlandSoil carbonGrasslandAridLand useLand use, land-use change and forestryClimate changeProductivityCarbon sequestrationAgroforestryCarbon cycleCarbon fibersHydrology (agriculture)Soil waterAgronomyEcosystemSoil scienceCarbon dioxideEcologyGeology

Abstract

fetched live from OpenAlex

Accounting for one quarter of China’s land area, the endorheic Shiyang River basin is a vast semi-arid to arid region in China’s northwest. Exploring the impact of changes in land use on this arid area’s carbon budget under global warming is a key component to global climate change research. Variation in the region’s soil carbon storage due to land use changes occurring between 1973 and 2012 was estimated. The results show that land use change has a significant impact on the soil carbon budget, with soil carbon storage having decreased by 3.89 Tg between 1973 and 2012. Grassland stored the greatest amount of soil carbon (114.34 Mg ha−1), whereas considerably lower carbon storage occurred in woodland (58.53 Mg ha−1), cropland (26.75 Mg ha−1) and unused land (13.47 Mg ha−1). Grasslands transformed into cropland, and woodlands degraded into grassland have substantially reduced soil carbon storage, suggesting that measures should be adopted to reverse this trend to improve soil productivity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.219
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it