Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.017 |
| Scholarly communication | 0.013 | 0.036 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.014 | 0.037 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".