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Record W3088839328

Macrolichen community structure in boreal forested wetlands on the island of Newfoundland, Canada

2019· dissertation· en· W3088839328 on OpenAlexaboutno aff
Tegan Padgett

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2019
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicLichen and fungal ecology
Canadian institutionsnot available
Fundersnot available
KeywordsWetlandSwampEcotoneHabitatGeographyEcologyBiodiversitySpecies richnessLichenIndicator speciesEcosystemBiology
DOInot available

Abstract

fetched live from OpenAlex

Forested wetlands provide important ecosystem services and vital habitat for numerous
\norganisms. Epiphytic macrolichens are a common and abundant group of organisms in
\nforested wetlands and, given their habitat specificity, they are of potential use as
\nindicators of forested wetlands and spatial boundaries. However, little is known about the
\ncommunity structure of macrolichens in forested wetlands. To address this, I first tested
\nfor differences in macrolichen communities and habitat associations between wetlands
\nand ecoregions. I found significant differences between forested wetland classes and
\necoregions and identified potential indicator species. Second, I tested for differences in
\nmacrolichen communities among swamps, ecotones, and adjacent upland forests. I found
\nthat macrolichen community richness and diversity were highest in swamps and lowest in
\nupland forests, and that macrolichen communities were significantly different among
\nswamps, upland forests, and their ecotones. The results of this research highlight the
\npotential use of macrolichens as indicators of forested wetlands and their spatial
\nboundaries.

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)0.001
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 score0.279
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.002
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 head0.021
GPT teacher head0.230
Teacher spread0.209 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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