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Record W4391426724 · doi:10.13031/ja.15532

Predicted Contribution of Snowmelt to Subsurface Drainage Discharge in Two Subsurface-Drained Fields in Southern Quebec and Ontario

2024· article· en· W4391426724 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of the ASABE · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsAgriculture and Agri-Food CanadaMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSnowmeltDrainageHydrology (agriculture)Subsurface flowGeologyEnvironmental scienceGroundwaterGeomorphologyGeotechnical engineeringSnowEcology

Abstract

fetched live from OpenAlex

Highlights RZ-SHAW model delivered a satisfactory simulation for winter subsurface drainage discharge. Snowmelt dominated the drainage discharge during the winter and spring snowmelt seasons in southern Quebec. Snowmelt contributed to 29% and 18% of winter and annual drainage discharge, respectively, in southern Ontario. Abstract. Late winter/early spring has been recognized as a critical period for subsurface drainage discharge and associated nutrient losses due to snowmelt and rainfall in croplands under cold, humid climates in North America and Europe. Although the detachment and transport processes of soil particles under snowmelt and rainfall are known to be different, studies quantifying the contribution of snowmelt to winter drainage discharge are limited. This study aims to investigate the contribution of snowmelt to subsurface drainage discharge at two winterized experimental sites in southern Quebec and Ontario, Canada, using observed data and the Root Zone Water Quality Model-Simultaneous Heat and Water model (RZ-SHAW model). The RZ-SHAW model was calibrated and validated against the measured snow depth and year-round subsurface drainage discharge data from 2021 to 2023 at St. Emmanuel, Quebec, and from 2000 to 2003 at Harrow, Ontario. RZ-SHAW demonstrated satisfactory Nash-Sutcliffe model efficiency values (NSE = 0.50) for simulating snow depth, soil temperature, and winter subsurface drainage discharge. The calibrated RZ-SHAW model was used to simulate the contribution of snowmelt to subsurface drainage for the two sites from 1990 to 2022. The snowmelt was predicted to contribute 55% and 44% of the winter and annual subsurface drainage discharge for the cropland in Southern Quebec. In contrast, for the southern Ontario site, the contribution of snowmelt to winter and annual subsurface drainage discharge was 29% and 18%, respectively. Considering that snowmelt in Southern Quebec contributed a significant fraction of winter and annual subsurface drainage discharge, more attention should be devoted to adapting and developing new winter management for nutrient loss in future research in the region. Keywords: Cold region, Cropland drainage, Drainage partition, RZ-SHAW, RZWQM2, Snowmelt, Soil freeze and thaw, Winter subsurface drainage.

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.

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)0.001
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 score0.520
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.006
GPT teacher head0.230
Teacher spread0.223 · 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