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Record W2152926658 · doi:10.3732/ajb.0800312

Constraints on leaf structural traits in wetland plants

2009· article· en· W2152926658 on OpenAlexafffundabout
Corina Vernescu, Peter Ryser

Bibliographic record

VenueAmerican Journal of Botany · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsLaurentian University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSpecific leaf areaBiologyLeaf sizeHerbaceous plantInterspecific competitionDry matterTraitBotanyPhotosynthesisLaminaAgronomyDry weightWetlandEcology

Abstract

fetched live from OpenAlex

A plant species' ecology is associated with leaf economics, characterized, e.g., by photosynthetic rate, construction costs, and leaf life span. Specific leaf area (SLA, leaf area per leaf dry mass) is often considered to be a key trait in this respect, explaining interspecific variation in leaf economics. To understand factors constraining the specific leaf area, we investigated size-related biomechanical constraints of the traits that determine the SLA-leaf thickness, leaf dry matter content and leaf density-and the constraints these traits exert on each other among 33 herbaceous wetland species of northern Ontario with a wide variety of leaf forms, ranging from wide laminar leaves to long, narrow, and relatively thick columnar leaves. Data from garden experiments were compared with field data. The results agree with biomechanical predictions that lamina thickness and leaf dry matter content are positively and leaf density (fresh mass per volume) negatively associated with leaf length. The traits also constrain each other, but these intertrait relationships are confounded by interspecific variation in leaf length. We conclude that for a full understanding of the adaptive significance of leaf structural design, it is essential to include leaf size in the considerations.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.361
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

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

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 head0.006
GPT teacher head0.237
Teacher spread0.231 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations24
Published2009
Admission routes3
Has abstractyes

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