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Record W2056194379 · doi:10.2135/cropsci2008.07.0423

Physiological Mechanisms Underlying Heterosis for Shade Tolerance in Maize

2009· article· en· W2056194379 on OpenAlexaffabout
Weidong Liu, M. Tollenaar

Bibliographic record

VenueCrop Science · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsHeterosisShadingBiologyAgronomyInbred strainGrain yieldHybridHorticultureGenetics

Abstract

fetched live from OpenAlex

ABSTRACT Heterosis in maize ( Zea mays L.) confers stress tolerance. To better understand the physiological mechanisms underlying the differential response of a maize hybrid (CG60 × MBS1236) and its parental inbred lines to shading stress, studies were conducted in a field hydroponic system in Ontario, Canada, from 2004 to 2006. Shading stress consisted of a 55% reduction in incident solar radiation and was implemented either for a 30‐ to 33‐d period before silking starting at the 7‐leaf tip stage, a 21‐d period during silking, or a 21‐d period after silking. Mean reduction in total dry matter at maturity (TDM) due to the shading treatments was 18%, and this reduction was similar for the three shading periods. Heterosis for grain yield was greater when plants were exposed to shading during the presilking and silking periods compared to the unshaded control. This increase was attributable to increased heterosis for both harvest index and TDM. In contrast, shading during the grain‐filling period did not increase heterosis for grain yield. Heterosis for grain yield was highly associated with heterosis for kernel number. Heterosis for kernel set was attributable, in part, to the relationship between kernel number and plant growth rate (PGR) during the period bracketing silking and the inherent lower PGR of the inbred lines as compared to the hybrid. Kernel set was also affected by shading during the presilking period, in particular, in one of the two inbred lines.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.950
Threshold uncertainty score0.279

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.083
GPT teacher head0.295
Teacher spread0.212 · 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

Citations39
Published2009
Admission routes2
Has abstractyes

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