MétaCan
Menu
Back to cohort
Record W2337039209 · doi:10.2135/cropsci2015.09.0556

Yield Response to Early Defoliation in Spring‐Planted Canola

2016· article· en· W2337039209 on OpenAlexafffundabout
Lena D. Syrovy, Steven J. Shirtliffe, Mark Zarnstorff

Bibliographic record

VenueCrop Science · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNitrogen and Sulfur Effects on Brassica
Canadian institutionsUniversity of Saskatchewan
FundersUniversity of Saskatchewan
KeywordsCanolaBiologyBrassicaAgronomyCropYield (engineering)

Abstract

fetched live from OpenAlex

Canola ( Brassica napus L.) is a high‐value summer annual crop grown in the northern Great Plains of Canada and the United States. During the early stages of its life, the crop may lose leaf area as a result of spring hail storms, frost, herbicides, animal grazing, and insects. However, little information is available on the effect of defoliation during vegetative growth on yield for spring‐planted canola. To address this gap, a five site‐year study was conducted in central Saskatchewan, Canada, to test canola recovery from partial (50%) or full (100%) defoliation at five stages before flowering (2‐, 4‐, 6‐, 8‐, and 10‐leaf stages). Partially defoliated canola plots yielded, on average, 92% of untreated control plots, while fully defoliated canola yielded 75% of controls. The yield response to defoliation timings varied; in one site‐year yield loss was minimized with earlier timings, while in the remaining site‐years, seed yield response was inconsistent or unrelated to timing. Our study shows that partial or full defoliation of summer‐cropped canola at any point during vegetative growth is likely to cause a yield loss. However the crop's substantial compensation for loss of foliage means that reseeding is rarely profitable.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score0.189

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.009
GPT teacher head0.252
Teacher spread0.243 · 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 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

Citations7
Published2016
Admission routes3
Has abstractyes

Explore more

Same venueCrop ScienceSame topicNitrogen and Sulfur Effects on BrassicaFrench-language works237,207