Lund-Potsdam-Jena Wetland Hydrology and Methane DGV Model (LPJ-WHyMe v1.3.1)
Bibliographic record
Abstract
This model product provides the Fortran 77 source code for the Lund-Potsdam-Jena (LPJ) Wetland Hydrology and Methane Dynamic Global Vegetation Model (LPJ-WHyMe v1.3.1), auxiliary C++ routines, ASCII and NetCDF input data, and NetCDF example output data. LPJ-WHyMe v1.3.1 simulates peatland hydrology, permafrost dynamics, peatland vegetation, and methane emissions. The model processes can be simulated on an area-averaged 0.5 or 1.0 degree grid cell basis at global, regional, or site scales and on a daily, monthly, or annual time step as appropriate. Input driver data are monthly mean air temperature, total precipitation, percentage of full sunshine, annual atmospheric CO2 concentration, and soil texture class. The simulation for each grid cell begins from "bare ground", requiring a "spin up" (under non-transient climate) of ca. 1,000 years to develop equilibrium vegetation, carbon, and soil structure. Model simulations compare favorably, with some exceptions, to field observations collected from peatland sites (e.g., Degero, Sweden; Lakkasuo, Finland; BOREAS Northern Study Area, Canada; and others) and non-peatland sites (e.g., Point Barrow, Alaska, and Spasskaya, Siberia). LPJ-WHyMe is a further development of LPJ-WHy, which dealt with the introduction of permafrost and peatlands into LPJ. Implementing peatlands in LPJ required the addition of two new plant functional types (PFTs) (flood tolerant C3 graminoids and Sphagnum mosses) to the already existing ten PFTs, the introduction of inundation stress for non-peatland PFTs, a slow-down in decomposition under inundation, and the addition of a root exudates pool. LPJ-WHyMe v1.3.1 adds a methane model subroutine. This model product has one compressed data file (*.zip) and seven companion files.
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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.009 |
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".