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Record W7162834169

Phosphorus Transport in Simulated Snowmelt Water as Influenced by Landscape and Soil Properties

2025· other· en· W7162834169 on OpenAlexfundno aff
Landon Orenchuk, Helen Baulch, Jeff Schoenau, Jane Elliott, C. Hogarth

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersWestern Grains Research Foundation
KeywordsSnowmeltPhosphorusHydrology (agriculture)Soil waterSurface runoff
DOInot available

Abstract

fetched live from OpenAlex

Phosphorus movement is an environmental concern. The movement of P from surface soil and residues can cause P movement downward in the landscape into lower slopes and depressions through snowmelt runoff and tillage. This P can ultimately make its way into sensitive surface water bodies. Snowmelt is a significant contributor to P in runoff that occurs in many cold climate regions, including the northern Great Plains. However, there has been minimal research assessing how topography and landscape position relate to soil properties and snowmelt runoff P. Using spatial sampling and soil analysis (including measurements of P sorption capacities and available P) in conjunction with small-scale snowmelt simulations from four farm fields in southern Saskatchewan, key factors were identified associated with elevated P in simulated runoff. Within fields, areas that have been related to high runoff potential include depressions in landscapes that have greater slope gradients and accumulated lab ile P, and headlands where there is overlap of fertilizer application. In more gently undulating topography, a consistent dominant influence of landscape position was not evident, likely relating to very different topographical contexts of the prairie fields and multiple factors affecting mobilization of P to runoff waters. Overall, high available P levels in the soil were associated with higher P concentrations in snowmelt runoff. In addition, soil P sorption capacity had an inverse relationship with P in snowmelt. In summary, areas of the landscape that have higher soil labile P and low P sorption capacity are areas that have high runoff potential risk. While landscape position can be a good predictor of soil and runoff water labile P concentrations, especially landscapes with substantive topographical variation, other factors also mediate P contained in runoff. Assessments of soil labile P across a landscape are considered a good indicator of P content of runoff originating from that point in the landscape.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.193
Threshold uncertainty score0.383

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.004
GPT teacher head0.150
Teacher spread0.146 · 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
Published2025
Admission routes1
Has abstractyes

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