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Record W2043894902 · doi:10.2134/agronj2009.0397

Timing, Effect, and Recovery from Intraspecific Competition in Maize

2010· article· en· W2043894902 on OpenAlexafffund
Eric R. Page, M. Tollenaar, Elizabeth A. Lee, Lewis Lukens, Clarence J. Swanton

Bibliographic record

VenueAgronomy Journal · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Agriculture, Food and Rural Affairs
KeywordsIntraspecific competitionInterspecific competitionCompetition (biology)AgronomyBiologyWeedBiomass (ecology)CropSeedlingPhenologyLeaf area indexPlant ecologyField experimentEcology

Abstract

fetched live from OpenAlex

In production agriculture, it is not uncommon for a crop to experience both intra‐ and interspecific competition during the normal course of development. Although the competition between crop plants (i.e., intraspecifc) is often considered independently of crop‐weed competition (i.e., interspecific), the mechanisms through which yields are reduced may be common to both. The objective of this study was to use the experimental structure of a critical time for weed removal study to examine the timing and effect of intraspecific competition on maize ( Zea mays L.) biomass accumulation and phenological development. A field trial was conducted in which maize stands were thinned from a higher to a lower density at six stages of development. Results indicated that intraspecific competition at densities of 8 and 16 plants m −2 did not affect maize biomass accumulation until the 14th and 12th leaf tip stages, respectively. Before these stages, maize seedling growth at 8 or 16 plants m −2 was not resource limited. Increases in leaf area index and specific leaf area at the onset of intraspecific competition, and the recovery of plants following the removal of competitors, suggest that reductions in the rate of crop growth and development may have been linked to competition for light quantity.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.666
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)0.0020.000

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 head0.200
Teacher spread0.190 · 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.

Study designObservational
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

Citations24
Published2010
Admission routes2
Has abstractyes

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