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Record W2989888360 · doi:10.1093/treephys/tpz114

Discerning the effects of phosphate status on the metabolism of hybrid poplar

2019· article· en· W2989888360 on OpenAlexaff
Letitia Da Ros, Raju Soolanayakanahally, Shawn D. Mansfield

Bibliographic record

VenueTree Physiology · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant nutrient uptake and metabolism
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of British Columbia
Fundersnot available
KeywordsPhosphateWillowResorptionChemistryNutrientMetabolismPhosphorusSalicaceaeOrganic acidBotanyInorganic ionsBiochemistryBiologyIonWoody plantEndocrinologyOrganic chemistry

Abstract

fetched live from OpenAlex

Accumulation of phosphate in leaves as external environmental phosphate concentrations increase has been observed across the plant kingdom. The excess storage of anions, such as phosphate, has various metabolic trade-offs, including a corresponding influx of counter-ions to maintain charge balance and/or the reduction in organic acid content to maintain internal pH. The leaves and roots of four hybrid poplar genotypes were tested for differences in metabolic response to increasing external phosphate and further effects on patterns of anion resorption among hybrid poplar and willow were explored. Organic acid concentrations increased or remained constant across treatments, suggesting that metabolic adjustments were made in response to greater influxes of inorganic cations rather than a response to increasing phosphate. During senescence, the hybrid poplar Tristis had higher sulfate and organic acid resorption, while hybrid willow, AAFC-5, had higher phosphate resorption proficiencies, suggesting differing anion remobilization mechanisms. Furthermore, phosphate accumulation was shown to continue well after bud-set in poplar hybrids, which may contribute to the low phosphorus resorption efficiency. This indicates that closely related species, with similar growth strategies, show preferential resorption toward different nutrients.

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 score0.508
Threshold uncertainty score0.149

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 head0.009
GPT teacher head0.191
Teacher spread0.182 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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