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Divergence among arctic and alpine populations of the annual, <i>Koenigia islandica</i> : morphology, life‐history, and phenology

2008· article· en· W1997481634 on OpenAlexaff
Ioan Wagner, Andrew M. Simons

Bibliographic record

VenueEcography · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsArcticPhenologyEcologyRange (aeronautics)LatitudeAlpine plantContext (archaeology)HabitatArctic ecologyGrowing seasonBiologyLife history theoryGeographyLife history

Abstract

fetched live from OpenAlex

Arctic and alpine habitats occur along complex environmental gradients, and over an extensive geographical range. Despite some selective forces common to these habitats, evolutionary divergence among populations of arctic and alpine plants along this gradient is expected. Of particular significance, both in the context of life‐history theory and for implications of climate change, are the few annual species that have adapted to the constraints of an unpredictable, short growing season. In this study, morphological, life‐history and phenological characters were found to differ significantly among six widely distributed populations of the arctic‐alpine annual Koenigia islandica. On the basis of morphology and life‐history traits, populations from high latitudes, with the exception of Svalbard, performed better in simulated arctic conditions, whereas the low latitude alpine plants from Colorado showed enhanced performance under simulated alpine conditions. On the basis of phenology, the six populations can be clearly grouped into arctic, high latitude alpine and alpine populations: arctic plants were found to develop and flower earliest; alpine plants latest. Because these results were obtained using seeds harvested from plants first grown through a complete generation in growth chambers, they indicate strong genetic differentiation. We discuss possible adaptive explanations for observed differences among the six geographically divergent populations.

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 score0.003
Threshold uncertainty score0.749

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 studies0.0000.002
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 head0.013
GPT teacher head0.200
Teacher spread0.187 · 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

Citations25
Published2008
Admission routes1
Has abstractyes

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