Global parameterization and validation of a two‐leaf light use efficiency model for predicting gross primary production across FLUXNET sites
Bibliographic record
Abstract
Abstract Light use efficiency (LUE) models are widely used to simulate gross primary production (GPP). However, the treatment of the plant canopy as a big leaf by these models can introduce large uncertainties in simulated GPP. Recently, a two‐leaf light use efficiency (TL‐LUE) model was developed to simulate GPP separately for sunlit and shaded leaves and has been shown to outperform the big‐leaf MOD17 model at six FLUX sites in China. In this study we investigated the performance of the TL‐LUE model for a wider range of biomes. For this we optimized the parameters and tested the TL‐LUE model using data from 98 FLUXNET sites which are distributed across the globe. The results showed that the TL‐LUE model performed in general better than the MOD17 model in simulating 8 day GPP. Optimized maximum light use efficiency of shaded leaves (εmsh) was 2.63 to 4.59 times that of sunlit leaves (εmsu). Generally, the relationships of εmsh and εmsu with εmax were well described by linear equations, indicating the existence of general patterns across biomes. GPP simulated by the TL‐LUE model was much less sensitive to biases in the photosynthetically active radiation (PAR) input than the MOD17 model. The results of this study suggest that the proposed TL‐LUE model has the potential for simulating regional and global GPP of terrestrial ecosystems, and it is more robust with regard to usual biases in input data than existing approaches which neglect the bimodal within‐canopy distribution of PAR.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".