Comment on egusphere-2023-1955
Bibliographic record
Abstract
Abstract. Modeling and predicting changes in the function and structure of the terrestrial biosphere and its feedbacks to climate change strongly depends on our ability to accurately represent interactions of the carbon and water cycles, and energy exchange. However, carbon fluxes, hydrological status and energy exchange simulated by process-based terrestrial ecosystem models are subject to significant uncertainties, largely due to the poorly calibrated parameters related to various processes. In this work, an adjoint-based data assimilation system (Nanjing University Carbon Assimilation System, NUCAS) was developed, which is capable of assimilating multiple observations to optimize process parameters of a satellite data driven ecosystem model–BEPS (Boreal Ecosystem Productivity Simulator). Data assimilation experiments were conducted to demonstrate the robustness and to investigate the feasibility and applicability of NUCAS on seven sites by assimilating the carbonyl sulfide (COS) fluxes, which were tightly related to the stomatal conductance and photosynthesis. Results showed that NUCAS is able to achieve a consistent fit to COS observations across various ecosystems. Comparing prior simulations with validation datasets, we found that the assimilation of COS can significantly improve the model performance in gross primary productivity, sensible heat, latent heat and even soil moisture. We also showed that the NUCAS is capable of constraining parameters from multiple sites simultaneously and achieving a good consistency to the single-site assimilation. Our results demonstrate that COS can provide strong constraints on parameters relevant to water, energy and carbon processes with the data assimilation system, and open new perspectives for better understanding of the ecosystem carbon, water and energy exchanges.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.017 | 0.008 |
| Insufficient payload (model declined to judge) | 0.190 | 0.129 |
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".