Susceptibility to low‐temperature photoinhibition and the acquisition of freezing tolerance in winter and spring wheat: The role of growth temperature and irradiance
Bibliographic record
Abstract
Five winter and five spring wheat (Triticum aestivum L.) cultivars were grown under either control conditions (20°C/250 photosynthetic photon flux density (PPFD) [μmol m−2 s−1]), high irradiance (20°C/800 PPFD) or at low temperature (either 5°C/250 PPFD or 5°C/50 PPFD). To eliminate any potential bias, the wheat cultivars were arbitrarily chosen without any previous knowledge of their freezing tolerance or photosynthetic competence. We show that the differential susceptibilities to photoinhibition exhibited between spring and winter wheat cultivars, as assessed by chlorophyll fluorescence cannot be explained on the basis of either growth irradiance or low growth temperature per se. The role of excitation pressure is discussed. We assessed the correlation between susceptibility to low‐temperature photoinhibition, maximum ribulose 1,5‐bisphosphate carboxylase‐oxygenase (EC 4.1.1.39) and NADP‐dependent malate dehydrogenase (EC 1.1.1.82) activities, chlorophyll and protein concentrations and freezing tolerance determined by electrolyte leakage. Susceptibility to photoinhibition is the only parameter examined that is strongly and negatively correlated with freezing tolerance. We suggest that the assessment of susceptibility to photoinhibition may be a useful predictor of freezing tolerance and field survival of cereals.
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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 | 0.000 |
| 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 | 0.000 |
| 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".