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Record W2947145004 · doi:10.1186/s41610-019-0116-5

Effect of elevated atmospheric carbon dioxide on the allelopathic potential of common ragweed

2019· article· en· W2947145004 on OpenAlexaff
Jichul Bae, Chaeho Byun, Yun Gyong Ahn, Jung Hyun Choi, Dowon Lee, Hojeong Kang

Bibliographic record

VenueJournal of Ecology and Environment · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAllelopathy and phytotoxic interactions
Canadian institutionsAgriculture and Agri-Food Canada
FundersNational Research Foundation of KoreaSeoul National University
KeywordsAllelopathyRagweedAmbrosia artemisiifoliaBotanyBiologySesquiterpeneGermination

Abstract

fetched live from OpenAlex

Allelopathy has been suggested as one potential mechanism facilitating the successful colonisation and expansion of invasive plants. The impacts of the ongoing elevation in atmospheric carbon dioxide (CO2) on the production of allelochemicals by invasive species are of great importance because they play a potential role in promoting biological invasion at the global scale. Common ragweed (Ambrosia artemisiifolia var. elatior), one of the most notorious invasive exotic plant species, was used to assess changes in foliar mono- and sesquiterpene production in response to CO2 elevation (389.12 ± 2.55 vs. 802.08 ± 2.69 ppm). The plant growth of common ragweed significantly increased in elevated CO2. The major monoterpenes in the essential oil extracted from common ragweed leaves were β-myrcene, dl-limonene and 1,3,6-octatriene, and the major sesquiterpenes were β-caryophyllene and germacrene-D. The concentrations of 1,3,6-octatriene (258%) and β-caryophyllene (421%) significantly increased with CO2 elevation. These findings improve our understanding of how allelochemicals in common ragweed respond to CO2 elevation.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.859
Threshold uncertainty score0.495

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.004
GPT teacher head0.182
Teacher spread0.178 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

Explore more

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