Gaining local accuracy while not losing generality — extending the range of gap model applications
Bibliographic record
Abstract
For the study of long-term processes in forests, gap models generally sacrifice accuracy (i.e., simulating system behavior in a quantitatively accurate manner) for generality (i.e., representing a broad range of systems’ behaviors with the same model). We selected the gap model ForClim to evaluate whether the local accuracy of forest succession models can be increased based on a parsimonious modeling approach that avoids the additional complexity of a 3D crown model, thus keeping parameter requirements low. We improved the representation of tree crowns by introducing feedbacks between (i) light availability and leaf area per tree and (ii) leaf area per tree and diameter growth rate to account for the self-pruning in real stands. The local accuracy of the new model, ForClim v2.9.5, was considerably improved in simulations at three long-term forest research sites in the Swiss Alps, while its generality was maintained as shown in simulations of potential natural vegetation along a broad environmental gradient in Central Europe. We conclude that the predictive ability of a model does not depend on its complexity, but on the reproduction of patterns. Most importantly, model complexity should be consistent with the objectives of the study and the level of system understanding.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".