Optimum Growth Temperatures of Three Species of Green Chloromonas Snow Algae from Upstate New York and the White Mountains, Arizona
Bibliographic record
Abstract
The optimum temperatures of three species of snow algae were studied using four strains of Chloromonas (Cr.) rosae v. psychrophila, six strains of Cr. tughillensis, and one strain of Cr. chenangoensis. These axenic strains were from Upstate New York except for two of Cr. rosae v. psychrophila from the White Mountains, Arizona. Temperatures tested were from 2.5 to 20°C. The high elevation subalpine Cr. rosae v. psychrophila from New York and Arizona grew from 4 to 20°C and had the greatest cell counts at 4 to 15°C. In contrast, the subalpine to temperate low elevation strains of Cr. tughillensis grew from 2.5 to 10°C and optimally at 2.5 or 5°C, and Cr. chenangoensis grew from 2.5 to 7.5°C and optimally at 2.5 and 5°C. Chloromonas tughillensis and Cr. chenangoensis belong to a genetic subclade with low temperature optima, whereas Cr. rosae v. psychrophila belongs to a subclade with broad temperature optima. In acclimation experiments, there were no significant differences in cell counts when acclimating two Adirondack, New York, strains of Cr. rosae v. psychrophila for two weeks prior to experiments vs. using non-acclimated strains that were moved from 4°C directly to 4, 10, 15, or 20°C. For Cr. tughillensis, four of six strains had significantly higher cell counts when grown at 2.5°C after acclimation at 7.5°C for five months. These are the first reports of temperature optima of snow algae from eastern North America.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".