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Record W3017330978 · doi:10.18910/75519

Conversations on Plant Sensing : Notes From the Field

2015· article· en· W3017330978 on OpenAlexaboutno aff
Natasha Myers

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and Biological Electrophysiology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsField (mathematics)Environmental resource managementPolitical scienceEnvironmental planningEnvironmental ethicsEnvironmental science

Abstract

fetched live from OpenAlex

plant and allow for the rapid and systemic movement of proteins, DNA, RNA, viruses, and other large molecules throughout a plant's tissues.These tiny structures were, I anticipated, crucial to the story of plant sensing.Plants have no nervous system to connect up their widely dispersed tissues, including the roots, shoots, stems, leaves, flowers, and fruits that make up their complex and filigreed bodies.Plasmodesmata transform what it means for a plant to be multicellular.Indeed, connected cells form what is known as a symplasm, a continuous cellular connection that extends through a plant (see for example Marzec & Kurczynska 2014).I had long imagined that these inter-cellular channels were what made it possible for a plant to perceive and propagate sensations through its widely distributed tissues.A remarkable feature of plants is that even as they can grow, move, and sense from so many distributed nodes, they cohere in a way that suggests that each root tip is connected to the meristem of each growing branch or bud.I had come to see a plant's manifold meristems, its million-fold nodes of growth, as 'centres of indetermination', each an ongoing experiment in and with the world, materializing what comes to matter for that branch or leaf or bud, now, and now, and now (see Deleuze 1986; Myers 2014b).Could plasmodesmata be the cellular structure that enables such a widely distributed and multiply interested body to cohere, to hang together?Did plasmodesmata endow plants with a nervelike network to propagate energies, intensities, and affects throughout its body?But perhaps I was getting ahead of myself.Perhaps my own near numinous mediations on plants were getting in the way of me listening to what the scientists were actually saying.33 Some biographical context is perhaps helpful here.In the midst of my training in plant molecular and developmental biology in the late 1990s, I was lured into new ways of thinking about plants by apprenticing with practitioners at the margins of mainstream science.Within weeks of completing my undergraduate degree in biology at McGill University, and just before I started a doctoral degree investigating the molecular genetics of flower development, I signed up for a course at Schumacher College in Devon, U.K. with Brian Goodwin (1994), Margaret Colquhoun (1996), and Henri Bortoft (1996).They introduced me to works by others, including Craig Holdrege (1996) and Lynn Margulis and Dorion Sagan (1995).In very little time, my thinking about life and science were utterly transformed.My connection to plants was also intensified through dance.I was a life-long dancer and a choreographer, and I found myself spending a lot of time outside the laboratory creating moving meditations and choreographies to explore plant movements, tropisms, rhythms, and temporalities.I enjoyed trying on plant movements to see how they felt propagating through my tissues.I visualized plant movement to explore how such imaginings could alter the contours of my morphological imaginary.Approaching dance as experimental inquiry, I explored ways of using my own body to help puzzle through chemical communication between the layered tissues of developing flowers and fruits.It was by playing through the possibilities of vegetal growth, propagation, photosynthesis, tropisms, and movements that I became sensitized to the wiles of plant life.Becoming with and alongside plants, I kept acquiring newly vegetalized sensory dexterities.I particularly enjoyed visualizing how communities of supracellular plants might form enmeshed subterranean rhizomes.I imagined these as excitable networks that could hum with an electric charge.Years later my

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)0.013
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.026
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0170.017
Scholarly communication0.0130.036
Open science0.0020.009
Research integrity0.0140.037
Insufficient payload (model declined to judge)0.0210.005

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 head0.050
GPT teacher head0.241
Teacher spread0.191 · 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 designQualitative
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

Citations138
Published2015
Admission routes1
Has abstractyes

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