Adaptation and Acclimation of Photosynthetic Microorganisms to Permanently Cold Environments
Bibliographic record
Abstract
Persistently cold environments constitute one of our world's largest ecosystems, and microorganisms dominate the biomass and metabolic activity in these extreme environments. The stress of low temperatures on life is exacerbated in organisms that rely on photoautrophic production of organic carbon and energy sources. Phototrophic organisms must coordinate temperature-independent reactions of light absorption and photochemistry with temperature-dependent processes of electron transport and utilization of energy sources through growth and metabolism. Despite this conundrum, phototrophic microorganisms thrive in all cold ecosystems described and (together with chemoautrophs) provide the base of autotrophic production in low-temperature food webs. Psychrophilic (organisms with a requirement for low growth temperatures) and psychrotolerant (organisms tolerant of low growth temperatures) photoautotrophs rely on low-temperature acclimative and adaptive strategies that have been described for other low-temperature-adapted heterotrophic organisms, such as cold-active proteins and maintenance of membrane fluidity. In addition, photoautrophic organisms possess other strategies to balance the absorption of light and the transduction of light energy to stored chemical energy products (NADPH and ATP) with downstream consumption of photosynthetically derived energy products at low temperatures. Lastly, differential adaptive and acclimative mechanisms exist in phototrophic microorganisms residing in low-temperature environments that are exposed to constant low-light environments versus high-light- and high-UV-exposed phototrophic assemblages.
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 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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".