Changes in growth parameters of Pseudomonas pseudoalcaligenes after ten months culturing at increasing temperature
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".