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Record W2017326811 · doi:10.1139/b11-003

Phenotypic and genotypic differentiation of<i>Vaccinium vitis-idaea</i>between coastal barrens and forests in Nova Scotia, Canada

2011· article· en· W2017326811 on OpenAlexaffvenueabout
Jennifer L. Balsdon, Tyler Smith, Jeremy Lundholm

Bibliographic record

VenueBotany · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant pathogens and resistance mechanisms
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsBiologyHabitatVacciniumEcologyNova scotiaPine barrensGenetic diversityEricaceaeGeographyPopulationDemography

Abstract

fetched live from OpenAlex

Coastal barrens and forests are very different environments, making it surprising that some plant species grow in both habitats. Vaccinium vitis-idaea L., common in both habitats, was studied for phenotypic and genotypic differences that may correlate with the different environments. Of the measured phenotypic traits, leaf thickness demonstrated the best response to differences between habitat types. Amplified fragment length polymorphisms were used to assess the genetic diversity of 85 V. vitis-idaea plants between habitats. The overall genotypic diversity (D = 0.99) and evenness (E = 0.77) from this study were higher than that found in other studies on V. vitis-idaea, and were likely influenced by the sampling methods used. Although the harsh environment of the coastal barrens was expected to increase clonal reproduction, we found no evidence of extensive cloning in either habitat type. An AMOVA revealed that genetic variation was highest (87.8%) within populations, and that V. vitis-idaea was not genetically distinct between the coastal barrens and forests. This outcome is consistent with the hypothesis that coastal barrens and forest habitats along the Nova Scotia coast represent extremes of a successional continuum, rather than discrete plant communities.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.263

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 studies0.0010.000
Scholarly communication0.0010.000
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 head0.017
GPT teacher head0.181
Teacher spread0.164 · 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
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

Citations8
Published2011
Admission routes3
Has abstractyes

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