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Record W2124289445 · doi:10.1098/rspb.2000.0976

Mild environmental stress elicits mutations affecting fitness in<i>Chlamydomonas</i>

2000· article· en· W2124289445 on OpenAlexaff
Shaun A. Goho, Graham Bell

Bibliographic record

VenueProceedings of the Royal Society B Biological Sciences · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsMutantChlamydomonasMutationMutation rateBiologyOsmotic shockGeneticsStress (linguistics)Animal scienceGene

Abstract

fetched live from OpenAlex

Cultures of Chlamydomonas were exposed to a range of relatively mild stresses for a period of 24 h. These stresses comprised high and low temperatures, osmotic stress, low pH, starvation and toxic stress. They were then allowed to recuperate for around ten vegetative generations under near-optimal conditions in unmodified minimal medium. Fitness was then assayed as the rate of division of isolated cells on agar. We found that there was a strong tendency for stressed cultures to have lower mean fitness and greater standardized variance in fitness than the negative controls which had been cultured throughout in unmodified minimal medium. The same tendency was shown, as expected, by positive controls which received mutagenic doses of ultraviolet irradiation. We concluded that the most reasonable interpretation of these observations is that mild stress increases the genomic rate of mutation. This appears to be the first time that this phenomenon has been noticed in eukaryotes. The response might be adaptive because lineages in which higher mutation rates are elicited by stress can be favourably selected through the production of a few mutants which are fortuitously well adapted to the stressful environment. Other interpretations are not excluded, however. Regardless of the mechanism involved, the elevation of mutation rates under stress will affect the rate of evolutionary response to environmental change and also the maintenance of sexuality.

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 score0.095
Threshold uncertainty score0.260

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 science0.0000.000
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 head0.010
GPT teacher head0.235
Teacher spread0.225 · 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

Citations82
Published2000
Admission routes1
Has abstractyes

Explore more

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