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Record W2955456263

System level carbon sequestration by riparian buffer systems as influenced by soil texture, vegetation type and age in southern Ontario

2019· dissertation· en· W2955456263 on OpenAlexaboutno aff
Sowthini Vijayakumar

Bibliographic record

VenueThe Atrium (University of Guelph) · 2019
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicSoil erosion and sediment transport
Canadian institutionsnot available
Fundersnot available
KeywordsCarbon sequestrationVegetation (pathology)Riparian zoneSoil textureTexture (cosmology)Environmental scienceRiparian bufferVegetation typeGeographySoil scienceForestryHydrology (agriculture)AgroforestryGeologySoil waterEcologyGeotechnical engineeringCarbon dioxideGrasslandComputer science
DOInot available

Abstract

fetched live from OpenAlex

The effect of soil texture, vegetation type and age on system level carbon (C) sequestration in eight riparian buffer systems (RBS) in southern Ontario was investigated. Biomass C sequestration was up to three times (247 Mg C ha-1) greater in deciduous buffers than in coniferous buffers (100 Mg C ha-1) regardless soil texture and age. Mature deciduous tree buffers in clay soils had the highest soil organic carbon (SOC - 177.62 ± 6.193 Mg C ha-1) while the young coniferous buffers in loam soil had the lowest (94.71 ± 6.193 Mg C ha-1). SOC sequestration (154 Mg C ha-1) was significantly higher (p<0.05) in mature buffers compared to adjacent agricultural fields (88 Mg C ha-1), irrespective of soil texture and vegetation type. All RBS had ~66% of SOC in the heavy fraction, indicating stable SOC. Soil NH4+-N was the predominant inorganic N source in all RBS.

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)0.000
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 score0.049
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.017
GPT teacher head0.193
Teacher spread0.176 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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