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Record W2591947530 · doi:10.1139/cjps-2014-221

Effect of climate change and use of improved varieties on barley and canola yield in Manitoba

2015· article· en· W2591947530 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueBioOne Complete (BioOne) · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicClimate change impacts on agriculture
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Alberta
Fundersnot available
KeywordsCanolaAgronomyYield (engineering)Climate changeCultivarHordeum vulgareGrain yieldEnvironmental scienceMathematicsBiologyPoaceaeEcology

Abstract

fetched live from OpenAlex

An, H. and Carew, R. 2015. Effect of climate change and use of improved varieties on barley and canola yield in Manitoba. Can. J. Plant Sci. 95: 127-139. A stochastic production function was estimated to investigate the effect of fertilizer inputs, changes in weather conditions and the use of improved varieties on barley and canola yields and its variability in Manitoba. Adoption of improved barley varieties did not have a significant effect on yield, while the adoption of herbicide-tolerant hybrid canola varieties was positively correlated with yield. An increasingly warmer climate in Manitoba is expected to have a slightly negative effect on mean barley yield and yield variance. In contrast, a warmer climate is expected to have a negligible effect on mean canola yield, but a positive effect on yield variability. Our results showed that a projected 50% increase in growing degree days would lead to a decrease of less than 1% in barley and canola 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.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.851
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.455
GPT teacher head0.254
Teacher spread0.201 · 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