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Record W2737545365 · doi:10.1139/cjb-2016-0275

Assessing the impacts of intra- and interspecific competition between <i>Triticum aestivum</i> and <i>Trifolium repens</i> on the species’ responses to ozone

2017· article· en· W2737545365 on OpenAlexvenueno aff
Analía I. Menéndez, Pedro E. Gundel, Laura M. Lores, M. Alejandra Martínez‐Ghersa

Bibliographic record

VenueBotany · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to elevated CO2
Canadian institutionsnot available
FundersFondo para la Investigación Científica y TecnológicaUniversidad de Buenos Aires
KeywordsTrifolium repensInterspecific competitionBiologyMonocultureRepensRhizobiumBiomass (ecology)Competition (biology)SymbiosisBotanyAgronomySowingOzoneGrowing seasonTropospheric ozonePerennial plantHorticultureEcologyChemistry

Abstract

fetched live from OpenAlex

Tropospheric ozone is considered to be the most phytotoxic air pollutant because of its oxidizing power. The main objective of this study was to analyze the effect of intra- and interspecific competition between Triticum aestivum L. and Trifolium repens L. on the responses to high concentrations of ozone of both species, and the role of the symbiotic relationship Rhizobium – T. repens on the abovementioned responses. Monocultures and mixtures of both species in different densities were sown. Pots were transferred to open top chambers either with 90–120 ppb ozone or without ozone. Ozone had an overall negative impact on leaf area and biomass production per individual plant. These responses were dependent on species and sowing density in monocultures, but were not changed by species proportion in the mixtures. There was a positive relationship between Rhizobium nodules and plant biomass, with a tendency for smaller plants to present lower number of nodules under ozone. These results suggest that competitive and mutualistic interactions could have a greater role in determining responses to novel air pollutants than species sensitivity to the xenobiotic, per se.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.950
Threshold uncertainty score0.552

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 studies0.0010.000
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 head0.044
GPT teacher head0.276
Teacher spread0.231 · 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

Citations13
Published2017
Admission routes1
Has abstractyes

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