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Record W2312732241 · doi:10.1139/b2012-057

Nitrogen, phosphorus, and light effects on reproduction and fitness of wild rice

2012· article· en· W2312732241 on OpenAlexvenueno aff
Lee Sims, John Pastor, Tali D. Lee, Brad Dewey

Bibliographic record

VenueBotany · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsBiologyInflorescencePhosphorusGerminationSeedlingNitrogenAgronomyBiomass (ecology)NutrientBotanyEcologyChemistry

Abstract

fetched live from OpenAlex

Plant fitness is typically correlated with total seed biomass or the number of seeds produced per plant, but the connection from seed production to seedlings the following year is seldom made. Seedling production in grasses, including wild rice ( Zizania palustris L.) is determined by the number of inflorescences produced, the number of seeds per inflorescence, the mean mass per seed, proportion of seeds that are filled, predation on seeds, and germination rates. Previous studies have shown that wild rice biomass production is limited primarily by nitrogen and secondarily by phosphorus and light. To test how nitrogen, phosphorus, and light modulate plant fitness, we evaluated the effects of nitrogen, phosphorus, and light on the above parameters. Nitrogen addition increased number of inflorescences, seeds per inflorescence, and mean seed mass, resulting in more seedlings produced, hence greater fitness, despite increased rice worm predation and lower germination rates of seeds compared to seeds from plants grown without nitrogen addition. Phosphorus additions and full sunlight also increased the number of seedlings per plant, mainly after nitrogen was added. Therefore, the maternal environment not only affects seed production but the number of seedlings that emerge the following year, especially with respect to nitrogen.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.242

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 head0.005
GPT teacher head0.210
Teacher spread0.205 · 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 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

Citations11
Published2012
Admission routes1
Has abstractyes

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