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Record W1520607785 · doi:10.5772/37108

The Chemical Kinetics of Shape Determination in Plants

2012· book-chapter· en· W1520607785 on OpenAlexafffund
M. David

Bibliographic record

VenueInTech eBooks · 2012
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Molecular Biology Research
Canadian institutionsBritish Columbia Institute of Technology
FundersNatural Sciences and Engineering Research Council of CanadaBritish Columbia Institute of Technology
KeywordsKineticsChemistryChemical engineeringPhysicsEngineeringClassical mechanics

Abstract

fetched live from OpenAlex

Murray (see Murray, 1989) and many others, through computer simulations of RD mechanisms for particular embryonic phenomena.The Turing mechanism for pattern formation was experimentally confirmed in 1990 with the CIMA redox reaction in gel reactors (Castets et al., 1990;Ouyang & Swinney, 1991).RD is now a broad and mature field (with consistently over 150 citations per year of Turing's paper over the past decade).This chapter focuses on the development of RD theory in plant morphogenesis, particularly in characterizing the interplay of pattern formation and domain growth (also see Holloway, 2010, for a short review).In contrast to animal embryogenesis, in which pattern formation generally occurs in fixed domains, the continuous growth of plants inextricably links chemical localization (patterning) and growth (shape change).Growth affects patterning: at the least patterns must be generated and maintained in the face of domain growth, since RD mechanisms (like other dynamic mechanisms) have harmonic solutions which depend on domain geometry, size and boundary conditions.But perhaps more importantly, patterning affects growth: plant cells expand through localized wall material addition or wall material relaxation, both chemically dependent processes.The patterning mechanisms which determine where expansion-associated chemicals are localized are therefore critical determinants in the shapes of plant tissues.The complex mechanical properties of plant cells are also important in the deformations leading to final overall shape, and will be discussed below in relation to chemical patterning.However, biochemical processes (interpreted broadly, e.g.genetic regulation, hormone response) generally precede shape change, and how these processes create spatial pattern is central to morphogenesis.(For further discussion on morphogenetic modelling in general, please see the recent reviews of Braybrook & Kuhlemeier, 2010;Grieneisen & Scheres, 2009;Jönsson & Krupinski, 2010; Roeder et al., 2011;and Zwieniecki & Dumais, 2011).Many developmental phenomena depend on a sequence of patterns, for example from simple extending tip growth to branching.RD theory provides a means for understanding the kinetic constraints involved in such symmetry-breaking transitions.The development of RD theory for growing domains, in conjunction with experimental tests, illuminates how chemical kinetics shape the plants around us, from ferns to spruce trees.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.003

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 head0.036
GPT teacher head0.255
Teacher spread0.218 · 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 designTheoretical or conceptual
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

Citations3
Published2012
Admission routes2
Has abstractyes

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