Summary of activities for DOE Award DE-FG02-02ER63444 for "Modeling dynamic vegetation for decadal to multi-century climate change studies"
Bibliographic record
Abstract
This is a summary of all activities that were funded by the DOE Award DE-FG02-02ER63444, ''Modeling dynamic vegetation for decadal to multi-century climate change studies'', during the period 09/01/2001-11/30/2004. The goal of this research has been to produce a process-based vegetation activity model suitable for coupling with a general circulation model (GCM) of the atmosphere, to simulate the biophysics of vegetation transpiration and photosynthesis, seasonal growth, and vegetation cover change. The model was to be developed within the NASA Goddard Institute for Space Studies (GISS) GCM. The model as envisioned in the original proposal was to be an adaptation of Dr. Friend's previous well-known dynamic vegetation model, HYBRID (Friend, et.al., 1997; Friend and White, 2000). After examining the issues of GCM model coupling, Dr. Friend realized some of the complexities of HYBRID would not be computationally suitable for the GCM. He wrote a review paper on ''big-leaf'' modeling issues (Friend, 2001), and concentrated on developing a new vegetation biophysics approach, which involved a computationally simply canopy-level conductance scheme (thus avoiding the problem of leaf-to-canopy scaling) and photosynthesis based on the work of Kull and Kruijt (1998), which distinguished the portion of leaf nitrogen that is photosynthetic. Dr. Friend presented the results photosynthesis/conductance scheme coupled to the Model II version of the GISS GCM (Hansen, et.al., 1983) in a talk at the 2002 AGU Fall Meeting in San Francisco. After Dr. Kiang arrived in April 2003, she, Dr. Friend, and Dr. Aleinov implemented the new scheme in the Model E version of the GISS GCM (Schmidt, et.al., accepted), and published results of improved surface temperatures and cloud cover in the Journal of Climate (Friend and Kiang, 2005). Till the end of the award period, Dr. Friend continued to develop a new vegetation growth model involving nitrogen allocation to light-stratified canopy layers compatible with the photosynthesis scheme. Dr. Kiang assembled a larger collaborative team, proposing a complete dynamic vegetation model with nitrogen cycling and vegetation change for GCMs. The model design was presented at the SciDac Conference in March 2004 in Charleston, SC, and the AGU Spring Meeting in May 2004 in Montreal.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.187 | 0.076 |
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".