Shedding some light on cold acclimation, cold adaptation, and phenotypic plasticity
Bibliographic record
Abstract
In the past, the role of light as an energy source was largely ignored in research focused on cold acclimation and freezing tolerance in plants. However, cold acclimation is an energy-requiring process. We summarize research illustrating that photoautrophs as diverse as cyanobacteria (Plectonema boryanum), green algae (Chlorella vulgaris, Dunaliella salina, Chlamydomonas raudensis), crop plants (Triticum aestivum L., Secale cereale L., Brassica napus L.), and conifers (Pinus banksiana) L.) tailor the structure and function of the photosynthetic apparatus to changes in temperature and irradiance to maintain cellular energy balance called photostasis. Modulation of either temperature or irradiance results in a similar imbalance in cellular energy that is sensed through changes in chloroplastic excitation pressure. Thus, concepts of photostasis and excitation pressure provide the context through which one can explain the congruence of phenotypic plasticity and photosynthetic performance associated with cold acclimation and photoacclimation. Photosynthetic organisms can sense changes in temperature and irradiance through modulation of the redox state of the photosynthetic electron transport chain, which, in turn, governs phenotype through the regulation of nuclear gene expression and chloroplast biogenesis. We suggest that elucidation of the molecular mechanism(s) by which excitation pressure regulates phenotypic plasticity and photosynthetic performance will be essential in addressing the challenge of maintaining or perhaps enhancing crop productivity under the suboptimal growth conditions predicted to occur as a consequence of climate change.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 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".