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Record W2097405085 · doi:10.1139/cjb-2013-0079

Geography and soil chemistry drive the distribution of fungal associations in lady’s slipper orchid,<i>Cypripedium acaule</i>

2013· article· en· W2097405085 on OpenAlexvenueno aff
William Bunch, Charles C. Cowden, Nina Wurzburger, Richard P. Shefferson

Bibliographic record

VenueBotany · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyBotanyOrchidaceaeEcology

Abstract

fetched live from OpenAlex

Mycorrhizal associations are required for the germination of orchids in nature. Recent studies reveal that distributions of arbuscular mycorrhizal and ectomycorrhizal fungal species are influenced by soil nutrient availability. However, it is unclear how soil nutrient availability influences mycorrhizal and root endophytic fungal association in orchids. Here we studied the pink lady’s slipper Cypripedium acaule Aiton, an orchid found typically in Pinus L. dominated forests of eastern North America, which associates with a diverse suite of fungi. We sampled 16 C. acaule populations across central and northern Georgia, USA. Soil samples were collected at the site of each plant and analyzed for organic matter, total carbon and nitrogen, calcium, ammonium, nitrate, and pH. Fungi present in the roots of each plant were identified from root samples using DNA analysis of key fungal barcoding genes. We then assessed the degree to which fungal associates corresponded to particular geographic, climatic, and soil factors via nonmetric multidimensional scaling. Our results indicate a broad association between geography, soil chemistry, and the distribution of root endophytic fungal associations in C. acaule. Importantly, this association may help explain why orchids with broad fungal associations are rare within landscapes. However, further research on the role of fungal availability in the soil is warranted.

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 score0.305
Threshold uncertainty score0.109

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 head0.012
GPT teacher head0.181
Teacher spread0.169 · 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

Citations33
Published2013
Admission routes1
Has abstractyes

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