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Record W2560056675 · doi:10.5539/jas.v9n1p15

Light Quality Effect on Corn Growth as Influenced by Weed Species and Nitrogen Rate

2016· article· en· W2560056675 on OpenAlexvenueno aff
Thomas R. Butts, Joshua J. Miller, Jon Derek Pruitt, Bruno C. Vieira, Maxwel C. Oliveira, Salvador Vergés Ramírez, John L. Lindquist

Bibliographic record

VenueJournal of Agricultural Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLight effects on plants
Canadian institutionsnot available
Fundersnot available
KeywordsWeedAgronomyCompetition (biology)BiologyShootGrowing seasonBiomass (ecology)NutrientWeed controlNitrogenChemistryEcology

Abstract

fetched live from OpenAlex

<p>Corn-weed competition has often been characterized as the competition for limited resources such as light quantity, water, and nutrients. However, growing evidence suggests that light quality, specifically the red:far red ratio (R:FR), is a crucial component to corn-weed interactions. Additionally, a reduction in the R:FR has shown to down-regulate plant genes similarly to a nitrogen (N) deficient environment. A greenhouse study was conducted to evaluate the effect of N stress and R:FR from common waterhemp, velvetleaf, and volunteer corn on corn growth and development. The R:FR for all three weed species tended to be similar but lower than a weed-free treatment. However, observations from the spectral response curves demonstrated significant changes in the patterns of light reflected from each weed species. In the N-sufficient environment, early-season (V5 corn growth stage) R:FR from all three weed species reduced corn height, leaf chlorophyll content, and shoot biomass while increasing fibrous root biomass. However, in the N-deficient environment, no effects were observed on corn growth from changes in light quality, indicating N stress was a greater limiting factor. These results highlight the importance of the critical weed-free period and the need for proper early-season weed management.</p>

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.553
Threshold uncertainty score0.274

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.010
GPT teacher head0.229
Teacher spread0.219 · 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

Citations12
Published2016
Admission routes1
Has abstractyes

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