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.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".