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Record W2794628731 · doi:10.1007/s11284-018-1595-z

Correction to: Tree hollows can affect epiphyte species composition

2018· article· en· W2794628731 on OpenAlexaff
Shinichi Tatsumi, Takayuki Ohgue, Wakana Azuma, Veera Tuovinen, Yume Imada, Akira Mori, Göran Thor, Åsa Ranlund

Bibliographic record

VenueEcological Research · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBryophyte Studies and Records
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsEpiphyteLichenBiologySpecies richnessEcologyMossBotany

Abstract

fetched live from OpenAlex

Abstract Tree hollows often harbor animals and microorganisms, thereby storing nutritive resources derived from their biological activities. The outflows from tree hollows can create unique microenvironments, which may affect communities of epiphytic organisms on trunk surfaces below the hollows. In this study, we tested whether the species richness and composition of epiphytic bryophytes (liverworts and mosses) and lichens differ above and below tree hollows of Aria japonica and Cercidiphyllum japonicum in a Japanese temperate forest. The species richness of epiphytic bryophytes and lichens did not differ above and below hollows; however, the species composition of bryophytes differed significantly above and below hollows. Indicator species analyses showed that the moss species Anomodon tristis and the liverwort species Porella vernicosa were significantly more common below than above hollows, while the liverwort species Radula japonica and four lichen species, including Leptogium cyanescens , occurred more frequently above than below hollows. Our results highlight that tree hollows can produce unique microenvironments on trunk surfaces that potentially contribute to the maintenance of epiphytic diversity on a local scale.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.087
GPT teacher head0.342
Teacher spread0.255 · 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.

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

Citations0
Published2018
Admission routes1
Has abstractyes

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