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Record W3126339672 · doi:10.1029/2020jg006076

Relationship Between Leaf Maximum Carboxylation Rate and Chlorophyll Content Preserved Across 13 Species

2021· article· en· W3126339672 on OpenAlexaffabout
Xiaojin Qian, Holly Croft, Jing M. Chen

Bibliographic record

VenueJournal of Geophysical Research Biogeosciences · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPhotosynthesisChlorophyllCmaxCarboxylationPhotosynthetic capacityBotanyChlorophyll aHorticultureBiologyAgronomy

Abstract

fetched live from OpenAlex

Abstract The leaf maximum carboxylation rate ( V cmax ) is a crucial parameter in determining the photosynthetic capacity of plants. Providing accurate estimates of leaf V cmax , that cover large geographic areas and incorporate plant seasonality is central to correctly predicting carbon fluxes within the terrestrial global carbon cycle. Chlorophyll, as the main photon‐harvesting molecule in leaves, is closely linked to plant photosynthesis. However, how the nature of the relationship between the leaf maximum carboxylation rate (scaled to 25°C; V cmax,25 ) and leaf chlorophyll content varies according to plant type is uncertain. In this study, we investigate whether a universal and stable relationship exists between leaf V cmax,25 and leaf chlorophyll content across different plant types and verify it using field experiments. Measurements of leaf chlorophyll content (Chl) and CO 2 response curves were made on 283 crop, shrub, tree, and vegetable leaves, across 13 species, in China and southern Ontario, Canada. A strong relationship was found between the leaf V cmax,25 and chlorophyll content across different plant types ( R 2 = 0.65, p < 0.001). Cross‐validation showed that the model performs well, producing an RMSE value of 15.4 μmol m −2 s −1 . The results confirm that leaf chlorophyll content can be a reliable proxy for estimating V cmax,25 , opening the door to accurate, spatially continuous estimates of V cmax,25 at the global scale.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.450

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.117
GPT teacher head0.328
Teacher spread0.211 · 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 designObservational
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

Citations52
Published2021
Admission routes2
Has abstractyes

Explore more

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