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Relationships between seed banks and spatial heterogeneity of North American alvar vegetation

2003· article· en· W2180390851 on OpenAlexaffabout
Kaeli E. Stark, Jeremy Lundholm, Douglas W. Larson

Bibliographic record

VenueJournal of Vegetation Science · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsSpecies richnessVegetation (pathology)HabitatEcologyPlant coverNational parkSpatial heterogeneityPlant communityVascular plantGeographyBiology

Abstract

fetched live from OpenAlex

Abstract. This is the first quantitative study of seed bank characteristics in North American alvar habitats. We assessed seed bank density, species richness, and species composition in 75 plots distributed among five alvar sites in Bruce Peninsula National Park, Ontario, Canada, each of which displayed areas of high and low vegetation cover within the alvar and a fully forested perimeter area. Forested habitats immediately adjacent to alvar patches contained minimal seed banks for species restricted to the alvar patches. Open alvars contained less than 1% seeds from woody forest species. This suggests that forest is not invading adjacent alvar habitat via seeds and that adjacent forest does not contain a reservoir of alvar seeds. When compared to areas on the alvar with high vascular plant cover, areas with low cover contained a slightly smaller viable seed bank, but seed banks from high and low vegetation cover plots had similar species composition and species richness. High vegetation cover plots had slightly higher mean and maximum soil depths compared with low cover plots, but no differences in other physical and chemical parameters. Thus, spatial heterogeneity in plant cover is associated only weakly with heterogeneity in below-ground factors. Despite the availability of seed and soil resources, vegetation dynamics are constrained in areas with low plant cover, and thus alvar community development seems to respond non-linearly to resource availability.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.017
Threshold uncertainty score0.445

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.022
GPT teacher head0.270
Teacher spread0.247 · 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

Citations20
Published2003
Admission routes2
Has abstractyes

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