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Does lack of available suitable habitat explain the patchy distributions of rare calcicole fern species?

2005· article· en· W2097471527 on OpenAlexaboutno aff
Matthew Wild, Daniel Gagnon

Bibliographic record

VenueEcography · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFern and Epiphyte Biology
Canadian institutionsnot available
Fundersnot available
KeywordsFernHabitatBiological dispersalEcologyRare speciesAbundance (ecology)BiologySeed dispersalGeographyPopulation

Abstract

fetched live from OpenAlex

In Canada, as in many countries, a relatively large number of fern species occur in specialized habitats and have low numbers of known populations containing few individuals. It has been suggested that the distribution of ferns is mostly determined by factors of climate and habitat due to relatively low limitations in ferns for dispersal and establishment. Here, we attempt to explain whether the local patchy distribution of three calcicole fern species rare in Canada ( Asplenium ruta‐muraria , Pellaea atropurpurea and Woodsia obtusa ) is due to a lack of available habitat. Analyses based on micro‐scale differences between sites occupied by the ferns and nearby, unoccupied sites did not reveal any significant differences, thereby indicating that the rarity of these species is not entirely driven by the rarity of their microhabitat at a local scale. Our results suggest that the widely accepted premise that ferns are not limited by dispersal or establishment should be reconsidered, as such limitations are the only likely explanation for empty available and suitable habitat.

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)0.002
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 score0.299
Threshold uncertainty score0.595

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.231
Teacher spread0.192 · 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

Citations32
Published2005
Admission routes1
Has abstractyes

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