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Record W4285800498 · doi:10.1111/1365-2745.13967

Leaf nitrogen from the perspective of optimal plant function

2022· article· en· W4285800498 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Ecology · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLeaf Properties and Growth Measurement
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersH2020 European Research CouncilAustralian Research CouncilNatural Environment Research CouncilSight Research UKSchmidt Family Foundation
KeywordsNitrogenCarboxylationPerspective (graphical)Function (biology)BotanyEnvironmental scienceAgronomyMathematicsBiologyChemistryEvolutionary biology

Abstract

fetched live from OpenAlex

Abstract Leaf dry mass per unit area (LMA), carboxylation capacity ( V cmax ) and leaf nitrogen per unit area (N area ) and mass (N mass ) are key traits for plant functional ecology and ecosystem modelling. There is however no consensus about how these traits are regulated, or how they should be modelled. Here we confirm that observed leaf nitrogen across species and sites can be estimated well from observed LMA and V cmax at 25°C ( V cmax25 ). We then test the hypothesis that global variations of both quantities depend on climate variables in specific ways that are predicted by leaf‐level optimality theory, thus allowing both N area to be predicted as functions of the growth environment. A new global compilation of field measurements was used to quantify the empirical relationships of leaf N to V cmax25 and LMA. Relationships of observed V cmax25 and LMA to climate variables were estimated, and compared to independent theoretical predictions of these relationships. Soil effects were assessed by analysing biases in the theoretical predictions. LMA was the most important predictor of N area (increasing) and N mass (decreasing). About 60% of global variation across species and sites in observed N area , and 31% in N mass , could be explained by observed LMA and V cmax25 . These traits, in turn, were quantitatively related to climate variables, with significant partial relationships similar or indistinguishable from those predicted by optimality theory. Predicted trait values explained 21% of global variation in observed site‐mean V cmax25 , 43% in LMA and 31% in N area . Predicted V cmax25 was biased low on clay‐rich soils but predicted LMA was biased high, with compensating effects on N area . N area was overpredicted on organic soils. Synthesis . Global patterns of variation in observed site‐mean N area can be explained by climate‐induced variations in optimal V cmax25 and LMA. Leaf nitrogen should accordingly be modelled as a consequence (not a cause) of V cmax25 and LMA, both being optimized to the environment. Nitrogen limitation of plant growth would then be modelled principally via whole‐plant carbon allocation, rather than via leaf‐level traits. Further research is required to better understand and model the terrestrial nitrogen and carbon cycles and their coupling.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.613
Threshold uncertainty score0.999

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)0.0020.000

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 head0.031
GPT teacher head0.200
Teacher spread0.168 · 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