Biochemical constrains limit the potential of the photochemical reflectance index as a predictor of effective quantum efficiency of photosynthesis during the winter spring transition in Jack pine seedlings
Bibliographic record
Abstract
Leaf reflectance spectral measurements are an emerging non-invasive technique that can be used to derive the photochemical reflectance index (PRI) to assess the physiological state of plants from leaf to ecosystem level. Changes in PRI are associated with changes in the xanthophyll cycle activity and provide an estimate of changes in the effective photochemical quantum efficiency (FII) during the growing season. However, we hypothesised that the correlation between PRI and FII might be poor when the xanthophyll cycle is primed for sustained thermal dissipation of the light energy absorbed. To test our hypothesis, we studied the recovery of winter acclimated Jack pine (Pinus banksiana Lamb.) seedlings that were exposed to different simulated spring recovery treatments in controlled environments. Different growth temperatures and light intensities were used to dissect the effect of these two factors on chlorophyll fluorescence, pigment composition and leaf reflectance. FII showed a clear response to temperature whereas PRI was mostly affected by light intensity. In contrast, the de-epoxidation state of the xanthophyll cycle pigments was both temperature and light dependent. Our data suggest that zeaxanthin-independent non-photochemical quenching is employed to various degrees in the different treatments. As a result, within the limits of our experimental setup, PRI could not explain the variation in FII. This indicates that an improved understanding of the different energy quenching mechanisms is critical to accurately interpret the PRI signal under environmental conditions where the predominant mode of excess energy dissipation does not involve a dynamic operation of the xanthophyll cycle, but a sustained mechanism of energy dissipation.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".