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Record W4321783187 · doi:10.1139/cjps-2022-0215

Historic climate change trends and impacts on crop yields in key agricultural areas of the prairie provinces in Canada: a literature review

2023· review· en· W4321783187 on OpenAlexafffundvenueabout
E. Mapfumo, D. S. Chanasyk, Dick Puurveen, Shannon Elton, Sachin Acharya

Bibliographic record

VenueCanadian Journal of Plant Science · 2023
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to elevated CO2
Canadian institutionsUniversity of AlbertaConcordia University of Edmonton
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPrecipitationClimate changeCroppingFrost (temperature)AgricultureGrowing seasonGeographyCropSnowAir temperatureGrowing degree-dayEnvironmental sciencePhysical geographyAgronomyEcologyForestryPhenologyBiologyMeteorologyArchaeology

Abstract

fetched live from OpenAlex

The objective of this literature review was to compile research findings on climate change and its impacts on crop production in Prairie Provinces of Canada. Our search strategy included finding primary literature articles from various databases. Seven articles reported increases in average and minimum air temperature over time in the Prairie Provinces of Canada. Increases in maximum air temperature were smaller than that for minimum air temperature. Growing degree days and corn heat units also increased over time, which has allowed for potential expansion of corn growth northwards. While overall increases in average annual precipitation and growing season precipitation have occurred in Canada between 1900 and 2021, western Canada showed increases in some regions but decreases in others. Off-season precipitation and snow cover duration in Canada have decreased since 1950. The number of frost-free days has increased across Canada, on the Prairies and southern Saskatchewan since 1900. Annual snowfall has decreased since 1950 and across Canada the annual maximum snow depth has also decreased. Overall, studies focusing on the Prairie Provinces in Canada have shown accelerated changes in several climate parameters over time, affecting cropping areas and crop yields.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.973
Threshold uncertainty score0.748

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.043
GPT teacher head0.245
Teacher spread0.203 · 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 designOther design
Domainnot available
GenreReview

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

Citations25
Published2023
Admission routes4
Has abstractyes

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