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Record W4413834409 · doi:10.1101/2025.08.25.672092

Understanding Shape and Residual Stress Dynamics in Rod-Like Plant Organs

2025· preprint· en· W4413834409 on OpenAlexfundno aff
Amir Porat

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldEngineering
TopicTree Root and Stability Studies
Canadian institutionsnot available
FundersIsrael Science FoundationCanada Research Chairs
KeywordsResidual stressResidualDynamics (music)Stress (linguistics)Biological systemMathematicsMaterials scienceBiologyPsychologyComposite materialAlgorithmPhilosophy

Abstract

fetched live from OpenAlex

Residual stresses are common in rod-like plant organs such as roots and shoots, arising from mechanical incompatibilities between tissues with differing intrinsic lengths. Although mechanical regulation of cell wall growth is well established, the role of internally generated stresses in organ-scale morphogenesis remains poorly understood. Here, we introduce a tractable theoretical framework that couples structural residual stresses to growth-driven shape dynamics. We represent rod-like organs as bundles of morphoelastic rods connected in parallel, each corresponding to a distinct concentric tissue layer. Assuming elastic strain-driven growth and rod-like symmetry, the resulting mechanical constraints yield transparent analytical relationships linking tissue heterogeneity to macroscopic observables such as axial strain rates, bending dynamics, and residual stress distributions. Using a minimal two-layer model representing the epidermis and inner tissues, we show how tissue incompatibilities can generate phenomena including effective autotropism, mechanical memory, and discontinuous growth dynamics following cell division. Overall, our framework demonstrates how residual stresses may contribute to plant morphogenesis and provides a foundation for future investigations of mechano-chemical growth control in plants.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.026
GPT teacher head0.206
Teacher spread0.180 · 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.

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

Citations1
Published2025
Admission routes1
Has abstractyes

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