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Grazing and soil carbon along a gradient of Alberta rangelands

2004· article· en· W4236681869 on OpenAlexaffabout
Darcy C. Henderson, B. H. Ellert, M. Anne Naeth

Bibliographic record

VenueJournal of Range Management · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland and Wildlife Management
Canadian institutionsUniversity of AlbertaAgriculture and Agri-Food Canada
Fundersnot available
KeywordsGrazingEnvironmental scienceSoil carbonRangelandVegetation (pathology)LitterOrganic matterSoil organic matterTotal organic carbonAgronomyEcologySoil waterSoil scienceAgroforestryBiology

Abstract

fetched live from OpenAlex

The regional scale response of soil carbon mass to long-term grazing exclusion was investigated in the Canadian Great Plains. Vegetation, litter, macro-organic matter and soil were sampled in paired grazed and ungrazed treatments from 9 independent locations along an environmental gradient in southern Alberta. Vegetation and litter carbon mass were greater on ungrazed treatments, but no consistent grazing effect was observed for macro-organic matter (roots, subsurface litter) or soil (fine particles 2mm) carbon mass per equivalent soil mass. Soil carbon in mixed grass prairie was positively correlated with clay content, but no grazing effect could be detected when this subset (n = 7) was analyzed by ANCOVA. Comparison of multiple sites with a consistent sampling and reporting method revealed no general trend in the response of soil carbon to grazing. Current range management practices to maintain range types in good to poor condition appear to be consistent with maintaining the soil organic matter pool in the northern Great Plains.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.266
Threshold uncertainty score0.535

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.193
Teacher spread0.187 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2004
Admission routes2
Has abstractyes

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