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Record W2291268260 · doi:10.1038/ismej.2015.240

Seasonal determinations of algal virus decay rates reveal overwintering in a temperate freshwater pond

2016· article· en· W2291268260 on OpenAlexafffund
Andrew M. Long, Steven M. Short

Bibliographic record

VenueThe ISME Journal · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyInfectivityTemperate climateOverwinteringVirusAlgaePersistence (discontinuity)EcologySeasonalityVirology

Abstract

fetched live from OpenAlex

To address questions about algal virus persistence (i.e., continued existence) in the environment, rates of decay of infectivity for two viruses that infect Chlorella-like algae, ATCV-1 and CVM-1, and a virus that infects the prymnesiophyte Chrysochromulina parva, CpV-BQ1, were estimated from in situ incubations in a temperate, seasonally frozen pond. A series of experiments were conducted to estimate rates of decay of infectivity in all four seasons with incubations lasting 21 days in spring, summer and autumn, and 126 days in winter. Decay rates observed across this study were relatively low compared with previous estimates obtained for other algal viruses, and ranged from 0.012 to 11% h(-1). Overall, the virus CpV-BQ1 decayed most rapidly whereas ATCV-1 decayed most slowly, but for all viruses the highest decay rates were observed during the summer and the lowest were observed during the winter. Furthermore, the winter incubations revealed the ability of each virus to overwinter under ice as ATCV-1, CVM-1 and CpV-BQ1 retained up to 48%, 19% and 9% of their infectivity after 126 days, respectively. The observed resilience of algal viruses in a seasonally frozen freshwater pond provides a mechanism that can support the maintenance of viral seed banks in nature. However, the high rates of decay observed in the summer demonstrate that virus survival and therefore environmental persistence can be subject to seasonal bottlenecks.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.642
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.010
GPT teacher head0.254
Teacher spread0.243 · 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 designBench or experimental
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
Published2016
Admission routes2
Has abstractyes

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