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Record W3045640762 · doi:10.3389/fmicb.2020.01721

Alpine Snow Algae Microbiome Diversity in the Coast Range of British Columbia

2020· article· en· W3045640762 on OpenAlexafffundabout
Kurt M. Yakimovich, Casey B. Engstrom, Lynne M. Quarmby

Bibliographic record

VenueFrontiers in Microbiology · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicPolar Research and Ecology
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaSimon Fraser UniversityGenome British Columbia
KeywordsBiologyAlgaeTaxonEcologyRange (aeronautics)ProtistBiodiversityTaxonomic rankFood webPredation

Abstract

fetched live from OpenAlex

Snow algae blooms contain bacteria, fungi, and other microscopic organisms. We surveyed 55 alpine snow algae blooms, collecting a total of 68 samples, from 12 mountains in the Coast Range of British Columbia, Canada. We used microscopy and rDNA metabarcoding to document biodiversity and query species and taxonomic associations. Across all samples, we found 173 algal, 2,739 bacterial, 380 fungal, and 540 protist/animalia operational taxonomic units (OTUs). In a previous study, we reported that most algal species were distributed along an elevational gradient. In the current study, we were surprised to find no corresponding distribution in any other taxa. We also tested the hypothesis that certain bacterial and fungal taxa co-occur with specific algal taxa. However, despite previous evidence that particular genera co-occur, we found no significant correlations between taxa across our 68 samples. Notably, seven bacterial, one fungal, and two cercozoan OTUs were widely distributed across our study regions. Taken together, these data suggest that any mutualisms with algae may not be taxon specific. We also report evidence of snow algae predation by rotifers, tardigrades, springtails, chytrid fungi, and ciliates, establishing the framework for a complex food web.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.287
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.198
Teacher spread0.186 · 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

Citations41
Published2020
Admission routes3
Has abstractyes

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