Best practices for calibration of forest landscape models using fine-scaled reference information
Bibliographic record
Abstract
Forest Landscape Models (FLMs) project responses to different climate, disturbance, and management scenarios and can inform decision-making that shapes ecosystems. However, use of FLM outputs by decision makers can be hampered by a lack of transparency and credibility in the calibration of modeled processes. Landscape modelers typically use fine-scaled (i.e., plot- or stand-level) information to calibrate the growth functions central to FLMs, but methods vary widely and are often poorly documented. We suggest best practices for calibration and assessment of tree growth in FLMs adapted from prior guidelines to increase rigor in ecological models and their application. Our proposed best practices include: (1) evaluating available information, (2) articulating assumptions, (3) accounting for scale, (4) formalizing model assessment stages, (5) grounding parameter ranges within empirical bounds, (6) considering parameter sensitivity, (7) verifying and corroborating output, (8) making iterative improvements, and (9) delivering sufficient documentation. We illustrate our approach across five case studies that involve a diversity of FLM designs centred on the tree-species, age-cohort structure available within the LANscape DIsturbance and Succession (LANDIS-II) modeling framework. We suggest that these best practices are applicable to many FLM platforms and provide the enhanced transparency essential for wider scientific acceptance of FLM projections.
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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 | 0.055 | 0.139 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".