The Atmosphere-Regolith-Vegetation Dynamic Global Vegetation Model (ARVE-DGVM)
Bibliographic record
Abstract
This is the original version of the ARVE Dynamic Global Vegetation Model (ARVE-DGVM) that was developed in 2006-2012 by Joe R. Melton and Jed O. Kaplan. Model development started when Kaplan was working at the University of Bern and Melton was a graduate student at the University of Victoria. The work was continued at the EPFL upon awarding of an SNF Grant to Kaplan funding a PDF for Melton. The ARVE-DGVM was intended to have intermediate complexity somewhere between the LPJ-DGVM (Sitch et al., 2003) and the Community Land Model (CLM, versions 3.0, 3.5, and 4.0) including aspects of both models. ARVE-DGVM had a unique variable length timestep for calculating all biogeophysical and biogeochemical processes, and a daily timestep for allocation and other vegetation dynamics. The variable timestep was to facilitate comparison between the model results and carbon and water fluxes measured with eddy covariance, which are typically more reliable during daytime (when there is more turbulence and vertical transport) than at night and to allow a lower computational cost than a fixed high frequency timestep. The ARVE-DGVM did work, but was computationally relatively intensive for the timescales of simulations it was intended for and provided little performance increase over CLM. Further development of ARVE-DGVM largely stopped when the developers moved on to new positions and other research priorities. This version of ARVE-DGVM is provided for archival and research purposes. Most of the code modules were last updated between July 2010 and June 2011, which was the last period of active development on the code.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".