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Record W3215391337 · doi:10.1111/sum.12780

Grassland soil organic carbon and the effects of irrigated cropping in Alberta, Canada

2021· article· en· W3215391337 on OpenAlexaffabout
Zhiping Wang, Xiaopeng Li, Rick Pelletier, Scott X. Chang, Edward W. Bork

Bibliographic record

VenueSoil Use and Management · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSoil carbonEnvironmental scienceGraminoidGrasslandSoil waterForbSpatial distributionTotal organic carbonAridGrazingAgronomySoil scienceEcologyGeographyChemistryEnvironmental chemistry

Abstract

fetched live from OpenAlex

Abstract High heterogeneity in the spatial distribution of soil organic carbon (SOC) in grasslands causes uncertainty in estimating its content and storage. In this study, we investigated the spatial distribution of SOC content and storage in the prairies of southern Alberta, Canada, and how it is affected by land use such as irrigated cropping and other environmental conditions such as cattle grazing, slope landscape position and dominant plant species. The mean SOC content was determined to be 11.5 g kg –1 (range: 8.9 to 22.4 g kg –1 ) in the 0–10 cm layer and 6.8 g kg –1 (range: 4.0 to 13.3 g kg –1 ) in the 10–30 cm layer; mean SOC storage was 1.59 kg C m –2 (range: 1.23 to 2.78 kg C m –2 ) in the 0–10 cm layer and 2.07 kg C m –2 (range: 1.21 to 3.62 kg C m –2 ) in the 10–30 cm layer. The SOC content was significantly affected by slope position in both the 0–10 and 10–30 cm layers, in the following order: bottom >middle > top position. Moreover, SOC storage was higher in sites dominated by shrubs than graminoid/forb communities. Thus, SOC content and storage had distinctly clustered spatial patterns throughout the study area and were significant differences between the 0–10 and 10–30 cm soil layers. Prior land‐use change from arid grassland to irrigated cropland increased SOC content and storage in bulk soils.

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.

How this classification was reachedexpand

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 score0.257
Threshold uncertainty score0.276

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 head0.005
GPT teacher head0.165
Teacher spread0.160 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2021
Admission routes2
Has abstractyes

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