Simulation of the Unexpected Photosynthetic Seasonality in Amazonian Evergreen Forests by Using an Improved Diffuse Fraction‐Based Light Use Efficiency Model
Bibliographic record
Abstract
Abstract Understanding the mechanism of photosynthetic seasonality in Amazonian evergreen forests is critical for its formulation in global climate and carbon cycle models. However, the control of the unexpected photosynthetic seasonality is highly uncertain. Here we use eddy‐covariance data across a network of Amazonian research sites and a novel evapotranspiration (E) and two‐leaf‐photosynthesis‐coupled model to investigate links between photosynthetic seasonality and climate factors on monthly scales. It reproduces the GPP seasonality (R2 = 0.45–0.69) with a root‐mean‐square error (RMSE) of 0.67–1.25 g C m−2 d−1 and a Bias of −0.03–1.04 g C m−2 d−1 for four evergreen forest sites. We find that the proportion of diffuse and direct sunlight governs the photosynthetic seasonality via their interaction with sunlit and shaded leaves, supported by a proof that canopy light use efficiency (LUE) has a strong linear relationship with the fraction of diffuse sunlight for Amazonian evergreen forests. In the transition from dry season to rainy season, incident total radiation (Q) decreased while LUE and diffuse fraction increased, which produced the large seasonal increase (~34%) in GPP of evergreen forests. We conclude that diffuse radiation is an important environmental driver of the photosynthetic seasonality in tropical Amazon forests yet depending on light utilization by sunlit and shaded leaves. Besides, the GPP model simulates the precipitation‐dominated GPP seasonality (R2 = 0.40–0.69) at pasture and savanna sites. These findings present an improved physiological method to relate light components with GPP in tropical Amazon.
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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 | 0.001 | 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".