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Enregistrement W3017330978 · doi:10.18910/75519

Conversations on Plant Sensing : Notes From the Field

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

Notice bibliographique

RevueInstitutional Repositories DataBase (IRDB) · 2015
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiquePlant and Biological Electrophysiology Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésField (mathematics)Environmental resource managementPolitical scienceEnvironmental planningEnvironmental ethicsEnvironmental science

Résumé

récupéré en direct d'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

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,013
score de la tête « metaresearch » (Gemma)0,026
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,021
Score d'incertitude au seuil0,071

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0130,026
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0030,002
Études des sciences et des technologies0,0170,017
Communication savante0,0130,036
Science ouverte0,0020,009
Intégrité de la recherche0,0140,037
Charge utile insuffisante (le modèle a refusé de juger)0,0210,005

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,050
Tête enseignante GPT0,241
Écart entre enseignants0,191 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations138
Publié2015
Routes d'admission1
Résumé présentoui

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