MétaCan
Menu
Back to cohort
Record W3039397041 · doi:10.1111/1462-2920.15151

Into the wild blueberry ( <scp> <i>Vaccinium angustifolium</i> </scp> ) rhizosphere microbiota

2020· article· en· W3039397041 on OpenAlexafffund
Simon Morvan, Hacène Meglouli, Anissa Lounès‐Hadj Sahraoui, Mohamed Hijri

Bibliographic record

VenueEnvironmental Microbiology · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyVacciniumRhizosphereBotanyBradyrhizobiumSymbiosisBacteria

Abstract

fetched live from OpenAlex

The ability of wild blueberries to adapt to their harsh environment is believed to be closely related to their symbiosis with ericoid mycorrhizal fungi, which produce enzymes capable of organic matter mineralization. Although some of these fungi have been identified and characterized, we still know little about the microbial ecology of wild blueberry. Our study aims to characterize the fungal and bacterial rhizosphere communities of Vaccinium angustifolium (the main species encountered in wild blueberry fields). Our results clearly show that the fungal order Helotiales was the most abundant taxon associated with V. angustifolium. Helotiales contains most of the known ericoid mycorrhizal fungi which are expected to dominate in such a biotope. Furthermore, we found the dominant bacterial order was the nitrogen-fixing Rhizobiales. The Bradyrhizobium genus, whose members are known to form nodules with legumes, was among the 10 most abundant genera in the bacterial communities. In addition, Bradyrhizobium and Roseiarcus sequences significantly correlated with higher leaf-nitrogen content. Overall, our data documented fungal and bacterial community structure differences in three wild blueberry production fields.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.834
Threshold uncertainty score0.995

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.006

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 head0.005
GPT teacher head0.161
Teacher spread0.156 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations65
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueEnvironmental MicrobiologySame topicMycorrhizal Fungi and Plant InteractionsFrench-language works237,207