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Record W4319461717 · doi:10.1111/nph.18800

Plant water use theory should incorporate hypotheses about extreme environments, population ecology, and community ecology

2023· article· en· W4319461717 on OpenAlexaff
Benjamin Blonder, L. M. T. Aparecido, Kevin R. Hultine, Danica Lombardozzi, Sean T. Michaletz, Bradley C. Posch, Martijn Slot, Klaus Winter

Bibliographic record

VenueNew Phytologist · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsUniversity of British Columbia
FundersArizona State UniversityU.S. Department of AgricultureNational Science Foundation
KeywordsEcologyCommunityPopulationPopulation ecologyEcological systems theoryEvolutionary ecologyBiologyEnvironmental resource managementEnvironmental scienceHabitatSociology

Abstract

fetched live from OpenAlex

Plant water use theory has largely been developed within a plant-performance paradigm that conceptualizes water use in terms of value for carbon gain and that sits within a neoclassical economic framework. This theory works very well in many contexts but does not consider other values of water to plants that could impact their fitness. Here, we survey a range of alternative hypotheses for drivers of water use and stomatal regulation. These hypotheses are organized around relevance to extreme environments, population ecology, and community ecology. Most of these hypotheses are not yet empirically tested and some are controversial (e.g. requiring more agency and behavior than is commonly believed possible for plants). Some hypotheses, especially those focused around using water to avoid thermal stress, using water to promote reproduction instead of growth, and using water to hoard it, may be useful to incorporate into theory or to implement in Earth System Models.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.004
Scholarly communication0.0020.005
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.063
GPT teacher head0.242
Teacher spread0.179 · 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

Citations47
Published2023
Admission routes1
Has abstractyes

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