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Record W2323753668 · doi:10.3389/fpls.2015.01182

Varied Growth Response of Cogongrass Ecotypes to Elevated CO2

2016· article· en· W2323753668 on OpenAlexaff
G. Brett Runion, Stephen A. Prior, Ludovic J. A. Capo-chichi, H. Allen Torbert, Edzard van Santen

Bibliographic record

VenueFrontiers in Plant Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to elevated CO2
Canadian institutionsAlberta Innovates
Fundersnot available
KeywordsEcotypeImperataPerennial plantWeedBiologyAgronomyBotanyHorticulture

Abstract

fetched live from OpenAlex

Cogongrass [Imperata cylindrica (L.) P. Beauv] is an invasive C4 perennial grass which is listed as one of the top ten worst weeds in the world and is a major problem in the Southeast US. Five cogongrass ecotypes [Florida (FL), Hybrid (HY), Louisiana (LA), Mobile (MB), and North Alabama (NA)] collected across the Southeast and a red-tip (RT) ornamental variety were container grown for 6 months in open top chambers under ambient and elevated (ambient plus 200 ppm) atmospheric CO2. Elevated CO2 increased average dry weight (13%) which is typical for grasses. Elevated CO2 increased height growth and both nitrogen and water use efficiencies, but lowered tissue nitrogen concentration; again, these are typical plant responses to elevated CO2. The HY ecotype tended to exhibit the greatest growth (followed by LA, NA, and FL ecotypes) whiles the RT and MB ecotypes were smallest. Interactions of CO2 with ecotype generally showed that the HY, LA, FL, and/or NA ecotypes showed a positive response to CO2 while the MB and RT ecotypes did not. Cogongrass is a problematic invasive weed in the southeastern U.S. and some ecotypes may become more so as atmospheric CO2 continues to rise.

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 score0.576
Threshold uncertainty score0.209

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.204
Teacher spread0.194 · 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

Citations10
Published2016
Admission routes1
Has abstractyes

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