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Record W2052129838 · doi:10.1139/x09-175

The influence of overstorey Populus on epiphytic lichens in subboreal spruce forests of British Columbia

2010· article· en· W2052129838 on OpenAlexafffundvenueabout
Jocelyn Campbell, Gary E. Bradfield, Cindy E. Prescott, Arthur L. Fredeen

Bibliographic record

VenueCanadian Journal of Forest Research · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLichen and fungal ecology
Canadian institutionsUniversity of Northern British ColumbiaUniversity of British Columbia
FundersNatural Resources CanadaUniversity of British ColumbiaCanadian Natural Resources Limited
KeywordsLichenUnderstoryEcologyCanopySpecies richnessEpiphyteAbundance (ecology)BotanyBiology

Abstract

fetched live from OpenAlex

The composition and abundance of lichen communities on conifer saplings beneath five overstorey tree species were compared at three subboreal forest site types in east-central British Columbia. Site-level differences in lichen communities were attributed to different levels of moisture and light limitations in the understorey. At sites with adequate moisture and light, cyanolichens were uniformly abundant and species rich on conifer saplings beneath different understorey species. However, at sites with moisture or light limitations, cyanolichens were more abundant and species rich on conifer saplings beneath overstorey Populus than on saplings beneath other overstorey tree species. Cyanolichen communities also showed greater species richness on conifer saplings beneath the Populus canopy than on the trunk of Populus itself. Differences in calcium, phosphorus, molybdenum, and manganese availability in throughfall precipitation failed to explain much of the variation in lichen community structure. These results suggest that Populus can facilitate cyanolichens under sub optimal moisture or light conditions by providing some, as yet unknown, factor that is critical to their establishment and growth.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.452
Threshold uncertainty score0.457

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.263
Teacher spread0.241 · 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

Citations32
Published2010
Admission routes4
Has abstractyes

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