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Record W4210613668 · doi:10.3390/agriculture12020233

Seasonal Changes in Phosphorus in Soils and Vegetation of Vegetated Filter Strips in Cold Climate Agricultural Systems

2022· article· en· W4210613668 on OpenAlexafffundabout
Kristen Kieta, Philip N. Owens, Jason Vanrobaeys, David A. Lobb

Bibliographic record

VenueAgriculture · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of ManitobaUniversity of Northern British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEnvironmental scienceSoil waterSurface runoffVegetation (pathology)Leaching (pedology)Hydrology (agriculture)NutrientAgronomySoil scienceEcologyGeologyBiology

Abstract

fetched live from OpenAlex

Vegetated filter strips (VFS) are a best management practice, designed to reduce sediment and nutrient runoff to surface waters in agricultural landscapes. In cold climates, phosphorus (P) can be released from VFS vegetation when it undergoes freeze–thaw cycles, making their utility in these regions an area needing further study. Research in Manitoba, Canada, investigated temporal changes in soil P concentrations and potential P loss from VFS from 2015 to 2017. Soil, harvestable vegetation, and residue samples were collected within VFS and control strips, and soils were analysed for Olsen P and Total P (TP). Harvestable vegetation and residue samples were analysed for TP. Results showed increases in Olsen P each spring and reductions each fall in soils at all sites. There was substantial loss of TP from the harvestable vegetation samples at all sites from November 2016 to May 2017, but residues in the VFS and control sites had a lower loss of TP over the same period. Vegetation was shown to release P over the winter into surface soils, and harvestable vegetation released significantly more P than residue, potentially leading to P loss from soils through surface runoff or leaching. Thus, harvesting vegetation may be a suitable VFS management strategy in cold climates.

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 score0.024
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.176
Teacher spread0.171 · 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

Citations7
Published2022
Admission routes3
Has abstractyes

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