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Record W7162826504

Carbon and nitrogen dynamics in soils amended with residues from major and minor crops

2025· other· en· W7162826504 on OpenAlexfundno aff
Mervin St. Luce, David Pelster, Manjula Bandara, Fadi Hassant

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersAgriculture and Agri-Food Canada
KeywordsCarbon fibersSoil waterNitrogenYield (engineering)Soil carbonCrop residue
DOInot available

Abstract

fetched live from OpenAlex

Little is known about how minor oilseed and special crops would affect carbon (C) sequestration potential and nitrogen (N) dynamics when included in diversified crop rotations in the Canadian prairies. Two 24-week aerobic incubation experiments (separately for C and N) were conducted with a mixture of above- and below-ground crop residues (4 g C kg-1 soil) from 13 crops plus a control (no crop residue) using two soils (silt loam and heavy clay). The addition of crop residues increased cumulative CO2-C emissions compared to the control. Across soils after 24 weeks, CO2-C emissions were higher for wheat and flax than for canola, canary seed, hemp, camelina and quinoa, and was higher in the silt loam than the clay soil. Compared to the control, the addition of crop residues reduced or increased net N mineralization depending on the crop residue in both soils. Overall, average net N mineralized was higher in the heavy clay than the silt loam soil, and was higher for quinoa than all other crops, being lowest for flax and wheat. The CO2-C emitted had positive associations with neutral detergent fibre (NDF), acid detergent fibre (ADF), cellulose, ADL/N ratio, C/N ratio and acid detergent lignin (ADL), but was negatively related with water-soluble C, total N and water-soluble N content. Conversely, net N mineralized was positively related to water-soluble C content, and negatively related to NDF, ADF, hemicellulose and cellulose. These findings are based on a short-term controlled environment study, hence, comprehensive studies under field conditions are warranted. © His Majesty the King in Right of Canada as represented by the Minister of Agriculture and Agri-Food, 2025

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.319

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.003

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 head0.151
Teacher spread0.147 · 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 designBench or experimental
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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