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Record W2052583680 · doi:10.1139/cjb-2012-0258

Genetic variation in the moss <i>Homalothecium lutescens</i> in relation to habitat age and structure

2013· article· en· W2052583680 on OpenAlexvenueno aff
Frida Rosengren, Nils Cronberg, Triin Reitalu, Honor C. Prentice

Bibliographic record

VenueBotany · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLichen and fungal ecology
Canadian institutionsnot available
FundersSvenska Forskningsrådet Formas
KeywordsBryophyteGrasslandBiologyEcologySpecies richnessHabitatMossGrazingBiodiversity

Abstract

fetched live from OpenAlex

Relationships between genetic (allozyme) variation and landscape age and structure were investigated in 17 calcareous grassland demes of the moss Homalothecium lutescens (Hedw.) H. Rob. on the Baltic island of Öland. Mean within-deme gene diversity (H S = 0.152) was moderate compared with other bryophyte studies, and the between-deme proportion of the total diversity (G ST = 0.100, Jost's D = 0.011) was low but significantly different from zero. Clonal mixing, measured as the proportion of two adjacent shoots having different haplotypes, was relatively high (mean 0.32 over all demes). H S was higher in old grassland fragments, but negatively related to vascular plant species richness. Allelic richness (A) was positively associated with the area of old (≥ 280 years) grassland in the surroundings: although demes in old grasslands are genetically more variable than those in younger grasslands, proximity to large areas of old grassland may promote genetic variability even in younger grassland demes. The importance of management continuity for species diversity has been stressed in many earlier grassland studies. Here, we conclude that grassland fragments with a long history of grazing continuity are also positively associated with variability at within-species level, as exemplified by the bryophyte H. lutescens.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.683
Threshold uncertainty score0.363

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 head0.009
GPT teacher head0.190
Teacher spread0.181 · 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

Citations15
Published2013
Admission routes1
Has abstractyes

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