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Ecophysiological differences among <i>Leymus mollis</i> populations across a subarctic dune system caused by environmental, not genetic, factors

2000· review· en· W2128285942 on OpenAlexaffabout
Éric Imbert, Gilles Houle

Bibliographic record

VenueNew Phytologist · 2000
Typereview
Languageen
FieldEnvironmental Science
TopicCoastal wetland ecosystem dynamics
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsSubarctic climateForeduneBiologyEcologyEnvironmental scienceAeolian processes

Abstract

fetched live from OpenAlex

Plant species that persist during succession, from the colonization to the stabilization stages, face major environmental changes. Such changes are believed to have significant effects on species performance. In subarctic coastal dune systems, Leymus mollis colonizes the embryo dunes, on the upper limit of the beach. It reaches its maximum density on the foredune, but also grows on older, stabilized ridges. This paper reports on the phenotypic variations of some ecophysiological traits associated with the persistence of L. mollis on a dune system on the east coast of Hudson Bay (northern Quebec). Leymus mollis ramets tend to have a lower net carbon assimilation rate and water use efficiency, and a higher substomatal CO2 concentration on the stabilized dune than on the foredune. However, these physiological differences cannot be explained by differences in leaf morphology or nitrogen content. Under controlled conditions, ecophysiological differences observed in the field disappear, suggesting that these are not genetic but determined by environmental changes along the foredune‐stabilized dune gradient. We propose that higher net carbon assimilation rate on the foredune might be related to higher sink strength in relation to the growth‐stimulating effect of sand burial.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.272
Teacher spread0.232 · 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 designObservational
Domainnot available
GenreReview

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

Citations8
Published2000
Admission routes2
Has abstractyes

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