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Record W3131813413 · doi:10.3390/plants10030440

The Interactive Effect of Elevated CO2 and Herbivores on the Nitrogen-Fixing Plant Alnus incana ssp. rugosa

2021· article· en· W3131813413 on OpenAlexafffund
Haoran Chen, John Markham

Bibliographic record

VenuePlants · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLegume Nitrogen Fixing Symbiosis
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFrankiaHerbivoreBiologyNitrogen fixationBotanyActinorhizal plantBiomass (ecology)Plant defense against herbivoryHorticultureAgronomyRoot noduleBacteria

Abstract

fetched live from OpenAlex

Many studies have found that future predicted CO2 levels can increase plant mass but dilute N content in leaves, impacting antiherbivore compounds. Nitrogen-fixing plants may balance their leaf C:N ratio under elevated CO2, counteracting this dilution effect. However, we know little of how plants respond to herbivores at the higher CO2 levels that occurred when nitrogen-fixing plants first evolved. We grew Alnus incana ssp. rugosa was grown at 400, 800, or 1600 ppm CO2 in soil collected from the field, inoculated with Frankia and exposed to herbivores (Orgyia leucostigma). Elevated CO2 increased nodulated plant biomass and stimulated the nitrogen fixation rate in the early growth stage. However, nitrogen-fixing plants were not able to balance their C:N ratio under elevated CO2 after growing for 19 weeks. When plants were grown at 400 and 1600 ppm CO2, herbivores preferred to feed on leaves of nodulated plants. At 800 ppm CO2, nodulated plants accumulated more total phenolic compounds in response to herbivore damage than plants in the non-Frankia and non-herbivore treatments. Our results suggest that plant leaf defence, not leaf nutritional content, is the dominant driver of herbivory and nitrogen-fixing plants have limited ability to balance C:N ratios at elevated CO2 in natural soil.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.232

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 head0.010
GPT teacher head0.205
Teacher spread0.195 · 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

Citations5
Published2021
Admission routes2
Has abstractyes

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