A two‐leaf rectangular hyperbolic model for estimating GPP across vegetation types and climate conditions
Bibliographic record
Abstract
Abstract There are mainly three types of gross primary production (GPP), including light use efficiency (LUE) model, rectangular hyperbolic model (RHM), and process‐based model (PBM). RHM is not widely used because its parameters, namely, quantum yield (α) and maximum photosynthetic rate (Pm), vary temporally with temperature and spatially with vegetation type under natural conditions. In the study, we present a temperature‐ and vegetation‐type‐adapted RHM by linking it to the Baldocchi's model to obtain the relationship between α‐Pm and Vcmax,25‐temperature to overcome the shortcomings of traditional RHM. The modified RHM (MRHM) coupled with a two‐leaf upscaling strategy makes it possible to accurate and fast estimation of GPP at large scale. Twenty‐two CO2 eddy‐covariance sites with different vegetation types, including evergreen needleleaf forest, deciduous broadleaf forest, grassland, and evergreen broadleaf forest, are used to evaluate the performance of MRHM for GPP estimation. The comparisons of the simulated GPP using MRHM with measured and Boreal Ecosystem Productivity Simulator‐simulated GPP demonstrate that the MRHM can simulate GPP as accurately as PBM and in the meantime with the advantage of simplicity as LUE model. These results show the promising potential of MRHM for accurately simulating GPP with relative high computational efficiency, providing an ideal alternative tool for large‐scale and long time series GPP simulations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".