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Record W4396532003 · doi:10.1016/j.pbi.2024.102542

Beauty is more than epidermis deep: How cell division and expansion sculpt the leaf spongy mesophyll

2024· article· en· W4396532003 on OpenAlexaff
Liyong Zhang, Chris Ambrose

Bibliographic record

VenueCurrent Opinion in Plant Biology · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Molecular Biology Research
Canadian institutionsUniversity of SaskatchewanAgriculture and Agri-Food Canada
Fundersnot available
KeywordsBiologyEpidermis (zoology)PhotosynthesisSpongy tissueBotanyCell divisionCell biologyPalisade cellAnatomyCell

Abstract

fetched live from OpenAlex

As the main location of photosynthesis, leaf mesophyll cells are one of the most abundant and essential cell types on earth. Forming the bulk of the internal tissues of the leaf, their size, shape, and patterns of interconnectivity define the internal structure and surface area of the leaf, which in turn determines the efficiency of light capture and carbon fixation. Understanding how these cellular traits are controlled and translated into tissue- and organ-scale traits, and how they influence photosynthetic performance will be key to our ability to improve crop plants in the face of a changing climate. In contrast to the extensive literature on the anatomical and physiological aspects of mesophyll function, our understanding of the cell-level morphogenetic processes underpinning mesophyll cell growth and differentiation is scant. In this review, we focus on how cell division, expansion, and separation are coordinated to create the intricate architecture of the spongy mesophyll.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.052
GPT teacher head0.312
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2024
Admission routes1
Has abstractyes

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