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Record W1970457195 · doi:10.1042/bst0380645

The role of chemical dynamics in plant morphogenesis

2010· review· en· W1970457195 on OpenAlexafffund
David M. Holloway

Bibliographic record

VenueBiochemical Society Transactions · 2010
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Molecular Biology Research
Canadian institutionsUniversity of VictoriaBritish Columbia Institute of Technology
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMorphogenesisBiological systemContext (archaeology)Pattern formationProcess (computing)Biochemical engineeringDynamics (music)Plant growthComputer scienceBiologyNanotechnologyPhysicsMaterials scienceBotanyEngineeringPaleontology

Abstract

fetched live from OpenAlex

In biological development, the generation of shape is preceded by the spatial localization of growth factors. Localization, and how it is maintained or changed during the process of growth, determines the shapes produced. Mathematical models have been developed to investigate the chemical, mechanical and transport properties involved in plant morphogenesis. These synthesize biochemical and biophysical data, revealing underlying principles, especially the importance of dynamics in generating form. Chemical kinetics has been used to understand the constraints on reaction and transport rates to produce localized concentration patterns. This approach is well developed for understanding de novo pattern formation, pattern spacing and transitions from one pattern to another. For plants, growth is continual, and a key use of the theory is in understanding the feedback between patterning and growth, especially for morphogenetic events which break symmetry, such as tip branching. Within the context of morphogenetic modelling in general, the present review gives a brief history of chemical patterning research and its particular application to shape generation in plant development.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score0.780

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.018
GPT teacher head0.255
Teacher spread0.236 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations14
Published2010
Admission routes2
Has abstractyes

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