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Record W4386601481 · doi:10.21203/rs.3.rs-3323035/v1

Crop Rotations and Changes in Saskatchewan Fertilizer Use: 1991-94 and 2016-19

2023· preprint· en· W4386601481 on OpenAlexafffundabout
Elisabeta Lika, Chelsea Sutherland, Savannah Gleim, Stuart J. Smyth

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsUniversity of Saskatchewan
FundersCanada First Research Excellence Fund
KeywordsCanolaCrop rotationFertilizerAgronomyTillageCropAgricultureEnvironmental scienceSustainabilityAgroforestryGeographyBiology

Abstract

fetched live from OpenAlex

Abstract Over the past 30 years, Saskatchewan, which holds over 40% of Canada’s cropland, has seen a shift in dryland crop production. Previously, fields were often left fallow with tillage as the primary weed control. The 1995 introduction of herbicide-tolerant canola transformed this, leading to continuous crop rotations and increased nitrogen-fixing pulse crops. This research, based on a 2020–2021 online survey, aimed to assess the impact of these changes on fertilizer use. Findings showed that while pulses positively impacted crop rotations, their effect on nitrogen fertilizer requirements varied. Additionally, GMHT canola required fewer fertilizers compared to the conventional canola in the 1991–1994 period. These changes highlight improved fertilizer efficiency, suggesting that expanding the pulse crop industry could further enhance prairie agriculture’s environmental sustainability.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.062
Threshold uncertainty score0.125

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.115
GPT teacher head0.356
Teacher spread0.241 · 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
Published2023
Admission routes3
Has abstractyes

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