Simulating Regional Vegetation‐climate Dynamics for Middle America: Tropical Versus Temperate Applications
Bibliographic record
Abstract
Abstract Regional vegetation‐climate modelling studies have typically focused on boreal or temperate ecosystems in North America and Europe, almost completely overlooking tropical ecosystems. We present the first results of simulated regional vegetation‐climate dynamics in Middle America as simulated by the model, LPJ‐GUESS. The Kappa statistic indicated poor agreement, with a Kappa value of 0.301. When we modified the Kappa statistic by aggregating cell sizes and using generalized biomes, the Kappa value increased to 0.543, indicating a fair agreement. Total LAI simulated from LPJ‐GUESS was strongly correlated to remotely sensed LAI values (r = 0.75). Our simulations indicate that fire frequency was overestimated in tropical moist forests and underestimated in savannas. This underestimation of fire resulted in an over‐simulation of dry tropical forest at the expense of savanna. We highlight additional reasons for the initially poor representation of vegetation in Middle America, including factors such as non‐parameterized plant functional types (desert shrub, cacti, and other succulents), rugged topography, and an insufficient representation of soil.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".