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Record W2109605669 · doi:10.3390/biology2020514

Composition, Diversity, and Stability of Microbial Assemblages in Seasonal Lake Ice, Miquelon Lake, Central Alberta

2013· article· en· W2109605669 on OpenAlexafffundabout
Anna R. Bramucci, Sukkyun Han, Justin Beckers, Christian Haas, Brian Lanoil

Bibliographic record

VenueBiology · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicPolar Research and Ecology
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of AlbertaAlberta InnovatesAlberta Innovates - Technology Futures
KeywordsBiologyEcologyBacteroidetesActinobacteriaMicrobial loopSea iceAlgaeIce coreOceanographyPhytoplanktonBacteriaGeologyNutrientPaleontology16S ribosomal RNA

Abstract

fetched live from OpenAlex

The most familiar icy environments, seasonal lake and stream ice, have received little microbiological study. Bacteria and Eukarya dominated the microbial assemblage within the seasonal ice of Miquelon Lake, a shallow saline lake in Alberta, Canada. The bacterial assemblages were moderately diverse and did not vary with either ice depth or time. The closest relatives of the bacterial sequences from the ice included Actinobacteria, Bacteroidetes, Proteobacteria, Verrucomicrobia, and Cyanobacteria. The eukaryotic assemblages were less conserved and had very low diversity. Green algae relatives dominated the eukaryotic gene sequences; however, a copepod and cercozoan were also identified, possibly indicating the presence of complete microbial loop. The persistence of a chlorophyll a peak at 25-30 cm below the ice surface, despite ice migration and brine flushing, indicated possible biological activity within the ice. This is the first study of the composition, diversity, and stability of seasonal lake ice.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.107
Threshold uncertainty score0.215

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.013
GPT teacher head0.228
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2013
Admission routes3
Has abstractyes

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