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Record W2122529231 · doi:10.1016/s0168-6496(03)00129-6

Changes in growth parameters of Pseudomonas pseudoalcaligenes after ten months culturing at increasing temperature

2003· article· en· W2122529231 on OpenAlexaff
Bihong Shi, Xuhua Xia

Bibliographic record

VenueFEMS Microbiology Ecology · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicListeria monocytogenes in Food Safety
Canadian institutionsUniversity of Ottawa
FundersUniversity of Hong Kong
KeywordsGompertz functionBiologyGrowth rateAnimal scienceStrain (injury)BotanyStatisticsAnatomyMathematics

Abstract

fetched live from OpenAlex

Abstract In this paper, we report the thermal adaptation of Pseudomonas pseudoalcaligenes, characterized as changes in growth parameters. Six clones derived from a single colony of P. pseudoalcaligenes were cultured in two different temperature regimes for 10 months, with three clones forming the control group, cultured at a constant temperature, and another three clones forming the high-temperature (HT) group, cultured at increasing temperature (from 41 to 47 degrees C). Three growth parameters were measured: the lag time (lambda), which is the period between the time of transfer to a new medium and the time when the cell replication starts; the maximum growth rate (mu(m)); and the maximum yield (A). These three parameters are major components of bacterial fitness. The Gompertz and logistic models were used to estimate these three parameters. The two models gave almost identical estimates, but the Gompertz model had R(2) values consistently larger than the logistic model. The HT clones had significantly shorter lambda, but higher mu(m) and A than the control clones when both were grown at the originally stressful temperature of 45 degrees C, suggesting significant thermal adaptation. Interestingly, the HT clones grew equally well as the control clones at 35 degrees C, i.e. improved performance at 45 degrees C was not associated with a reduced performance at 35 degrees C.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.018
GPT teacher head0.253
Teacher spread0.235 · 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

Citations17
Published2003
Admission routes1
Has abstractyes

Explore more

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