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Record W2797595484 · doi:10.1139/cjb-2017-0209

Forest structure and site conditions of boreal felt lichen (<i>Erioderma pedicellatum</i>) habitat in Cape Breton, Nova Scotia, Canada

2018· article· en· W2797595484 on OpenAlexaffvenueabout
Tom Power, Robert P. Cameron, Thomas H. Neily, Benjamin A. Toms

Bibliographic record

VenueBotany · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLichen and fungal ecology
Canadian institutionsSydney Steel (Canada)
Fundersnot available
KeywordsBasal areaLichenBiologySphagnumAbies balsameaBotanyForestryBorealEcologyPeatGeographyBalsam

Abstract

fetched live from OpenAlex

Boreal felt lichen [Erioderma pedicellatum (Hue) P.M. Jorg. (1972)] occurs on mainland Nova Scotia as well as Cape Breton, growing almost entirely on balsam fir [Abies balsamea (L.) Mill.] in wet coastal forests. A Geographical Information System (GIS) based predictive model for E. pedicellatum habitat in Nova Scotia has facilitated surveys and guided conservation. We used this model to examine the relationship between presence of E. pedicellatum and forest structure (tree DBH, height, age, and crown closure, inter-tree distance, basal area of live and dead trees, and percent cover of shrubs, herbs, Sphagnum spp., and other mosses), and site conditions (topographic position, slope, aspect, and drainage) as well as the presence of lichen indicator species. Erioderma pedicellatum sites had significantly older trees, higher density of live trees, lower crown closure, lower basal area of live Picea mariana (Mill.) Britton, Sterns &amp; Poggenb., lower basal area of live trees, higher basal area of dead trees, higher Sphagnum spp. cover, and lower shrub cover than unoccupied habitat. Erioderma pedicellatum sites were significantly less well drained and occurred on steeper slopes with a north or east aspect. Four macrolichens (Coccocarpia palmicola, Platismatia norvegica, Lobaria scrobiculata, and Sphaerophorus globosus) occurred at a significantly higher frequency at E. pedicellatum sites.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.477
Threshold uncertainty score0.499

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 head0.007
GPT teacher head0.202
Teacher spread0.195 · 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

Citations8
Published2018
Admission routes3
Has abstractyes

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