Simulating terrestrial carbon fluxes using the new biosphere model “biosphere model integrating eco‐physiological and mechanistic approaches using satellite data” (BEAMS)
Bibliographic record
Abstract
In this study we present a new biosphere model called the Biosphere model integrating Eco‐physiological And Mechanistic approaches using Satellite data (BEAMS). BEAMS provides a new method of calculating the environmental stress affecting plant growth (Stress). Stress is calculated eco‐physiologically using a photosynthesis model and stomatal conductance formulation, providing a more realistic result than previous models. Stress values are used to estimate Gross Primary Production (GPP) estimates via the light use efficiency concept. We used BEAMS, including our new Stress approach, to investigate global spatial and temporal patterns of net primary production (NPP) and net ecosystem production (NEP). BEAMS was run for the years 1982–2000 using global scale satellite and climate data. Comparison of model results with observational measurements at flux sites reveals that GPP values predicted by BEAMS agree with measured GPP. Obtained Stress values were compared with those of MOD17 and CASA; the three methods produce contrasting spatial patterns. Upon comparing predicted and observed NPP, the pattern of NPP for each plant functional type can be adequately estimated. In terms of trend analysis, NPP increased for the years 1982–2000 in most regions. Different NPP trends were observed in Europe, Russia, and northeast Canada than those proposed by Nemani et al. (2003); we attribute these differences to climate‐related processes. Simulated interannual variations in global NEP are similar to results from inverse modeling. A sensitivity study of obtained NEP shows that the interannual variability in NEP is strongly controlled by air temperature, precipitation, CO2, and the fraction of absorbed photosynthetically active radiation.
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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 | 0.001 |
| 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 | 0.000 |
| 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".