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Record W2141882414 · doi:10.1111/nph.12762

Effects of elevated <scp>CO</scp><sub>2</sub> on the extractable amino acids of leaf litter and fine roots

2014· article· en· W2141882414 on OpenAlexfundno aff
Sara Top, T. R. Filley

Bibliographic record

VenueNew Phytologist · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to elevated CO2
Canadian institutionsnot available
FundersCanadian Forest ServiceU.S. Forest ServiceNational Science Foundation
KeywordsAmino acidChemistryPlant litterLitterBiogeochemical cycleNitrogenCarbon fibersHorticultureBotanyAgronomyEnvironmental chemistryNutrientBiologyBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Elevated atmospheric CO2 concentrations can change chemistry and input rate of plant tissue to soil, potentially influencing above- and below-ground biogeochemical cycles. Given the important role played by leaf and root litter chemistry in controlling ecosystem function and vulnerability to environmental stresses, we investigated the hydrolyzable amino acid distribution and concentration in leaf and fine root litter among control and elevated CO2 treatments at the Rhinelander free air CO2 enrichment (FACE) experiment (WI, USA). We extracted hydrolyzable amino acids from leaf litter and fine (< 2 mm) roots at three depths for both control and elevated CO2 plots. We found that elevated CO2 decreased the proportion of total leaf amino acid carbon (C), but had no effect on total leaf amino acid nitrogen (N). There was no treatment effect for total root amino acid N or amino acid C for any depth. The decrease in leaf amino acids is probably a result of the shift of protein compounds to more structural compounds. Despite the decrease in leaf amino acid C concentrations, the overall increase in annual plant production under elevated CO2 would result in an increase in plant amino acids to the 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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.298
Threshold uncertainty score0.284

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.012
GPT teacher head0.209
Teacher spread0.197 · 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

Citations16
Published2014
Admission routes1
Has abstractyes

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