Seasonal acclimation of photosystem II in Pinus sylvestris. I. Estimating the rate constants of sustained thermal energy dissipation and photochemistry
Bibliographic record
Abstract
Acclimation of the partitioning of absorbed light energy in Photosystem II (PSII) between photochemical and non-photochemical processes includes short-term adjustments that are rapidly reversed in the dark and seasonal acclimation processes that are unaffected by dark acclimation. Thus, by using dark-acclimated leaves to study the seasonal acclimation of PSII, the confounding effect of short-term adjustments is eliminated. The maximum quantum yield of photochemistry, estimated by chlorophyll fluorescence analysis as F(v)/F(m), where F(v) = (F(m) - F(o)), and F(m) and F(o) are maximum and minimum chlorophyll fluorescence, respectively, has been widely used to follow the seasonal acclimation of PSII, because it is measured in dark-acclimated leaves. Seasonal changes in F(v)/F(m) can be caused by adjustments in either the photochemical capacity in PSII, or the capacity of thermal dissipation in PSII, or both. However, there is a lack of chlorophyll fluorescence parameters that can distinguish between these processes. In this study, we introduce two new parameters: the rate constants of sustained thermal energy dissipation (k(NPQ)) and of photochemistry (k(P)). We estimated k(NPQ) and k(P) from dark-acclimated F(o) and F(m) measured during spring recovery of photosynthesis in Scots pine (Pinus sylvestris L.) trees. We suggest that k(NPQ) and k(P) be used to study the mechanisms underlying the observed seasonal acclimation in PSII, because these parameters provide quantitative data that complement and extend F(v)/F(m) measurements.
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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) | 0.000 | 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".