Effects of low temperature stress on excitation energy partitioning and photoprotection in Zea mays
Bibliographic record
Abstract
Analysis of the partitioning of absorbed light energy within PSII into fractions utilised by PSII photochemistry (FPSII), thermally dissipated via ?pH- and zeaxanthin-dependent energy quenching (FNPQ) and constitutive non-photochemical energy losses (Ff,D) was performed in control and cold-stressed maize (Zea mays L.) leaves. The estimated energy partitioning of absorbed light to various pathways indicated that the fraction of FPSII was twofold lower, whereas the proportion of thermally dissipated energy through FNPQ was only 30% higher, in cold-stressed plants compared with control plants. In contrast, Ff,D, the fraction of absorbed light energy dissipated by additional quenching mechanism(s), was twofold higher in cold-stressed leaves. Thermoluminescence measurements revealed that the changes in energy partitioning were accompanied by narrowing of the temperature gap (?TM) between S2/3QB- and S2QA- charge recombinations in cold-stressed leaves to 8°C compared with 14.4°C in control maize plants. These observations suggest an increased probability for an alternative non-radiative P680+QA- radical pair recombination pathway for energy dissipation within the reaction centre of PSII in cold-stressed maize plants. This additional quenching mechanism might play an important role in thermal energy dissipation and photoprotection when the capacity for the primary, photochemical (FPSII) and zeaxanthin-dependent non-photochemical quenching (FNPQ) pathways are thermodynamically restricted in maize leaves exposed to cold temperatures.
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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 | 0.001 |
| 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 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".