How well do growing season dynamics of photosynthetic capacity correlate with leaf biochemistry and climate fluctuations?
Bibliographic record
Abstract
Accurate values of photosynthetic capacity are needed in Earth System Models to predict gross primary productivity. Seasonal changes in photosynthetic capacity in these models are primarily driven by temperature, but recent work has suggested that photoperiod may be a better predictor of seasonal photosynthetic capacity. Using field-grown kudzu (Pueraria lobata (Willd.) Ohwi), a nitrogen-fixing vine species, we took weekly measurements of photosynthetic capacity, leaf nitrogen, and pigment and photosynthetic protein concentrations and correlated these with temperature, irradiance and photoperiod over the growing season. Photosynthetic capacity was more strongly correlated with photoperiod than with temperature or daily irradiance, while the growing season pattern in photosynthetic capacity was uncoupled from changes in leaf nitrogen, chlorophyll and Rubisco. Daily estimates of the maximum carboxylation rate of Rubisco (Vcmax) based on either photoperiod or temperature were correlated in a non-linear manner, but Vcmax estimates from both approaches that also accounted for diurnal temperature fluctuations were similar, indicating that differences between these models depend on the relevant time step. We advocate for considering photoperiod, and not just temperature, when estimating photosynthetic capacity across the year, particularly as climate change alters temperatures but not photoperiod. We also caution that the use of leaf biochemical traits as proxies for estimating photosynthetic capacity may be unreliable when the underlying relationships between proxy leaf traits and photosynthetic capacity are established outside of a seasonal framework.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".