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Record W2080209459 · doi:10.1139/cjb-2014-0214

Relative growth rates and secondary compounds in epiphytic lichens along canopy height gradients in forest gaps and meadows in inland British Columbia

2015· article· en· W2080209459 on OpenAlexvenueaboutno aff
Massimo Bidussi, Yngvar Gauslaa

Bibliographic record

VenueBotany · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLichen and fungal ecology
Canadian institutionsnot available
Fundersnot available
KeywordsLichenEpiphyteBiologyEcologyCanopyHabitatMicrositeOld-growth forestThallusBotanySeedling

Abstract

fetched live from OpenAlex

We explore relative growth rates (RGRs) and carbon-based secondary compounds (CBSCs) in epiphytic lichens along height-above-the-ground gradients. The chlorolichen (Letharia vulpina (L.) Hue), the cephalolichen (Lobaria pulmonaria (L.) Hoffm.), and the cyanolichens (Lobaria hallii (Tuck.) Zahlbr., Nephroma helveticum Ach.) were attached to branches at 0.5–3.0 m heights of young spruce trees transplanted for 1 year in forest gaps and in old meadows of an inland valley in British Columbia. Cephalolichen and cyanolichen RGRs were highest in forest gaps, whereas the chlorolichen grew faster in meadows with twice as much light as forest gaps. Transplantation height did not influence lichen temperature or RGRs, despite height-dependent light increases. CBSCs were highest in the chlorolichen (13%), followed by the cephalolichen (5%) and the cyanolichens (1% and 0%). CBSC concentrations increased with thallus size, and were significantly higher in forest gaps mainly for L. pulmonaria. Only one minor CBSC in each species varied with height. The slow growth of cephalolichen and cyanolichens in meadows is consistent with these lichens’ preferences for forested habitats. Cold air ponding from snow-capped mountains was probably strong enough to form enough nocturnal dew to support reasonable lichen growth at all heights. The high species-specific and the low habitat-specific CBSC variations are consistent with constitutive CBSC defense levels in studied lichens.

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 score0.810
Threshold uncertainty score0.926

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 head0.015
GPT teacher head0.205
Teacher spread0.191 · 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

Citations16
Published2015
Admission routes2
Has abstractyes

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