A network approach for evaluating and communicating forest change models
Bibliographic record
Abstract
Summary 1. Knowledge of forest change is often formalized in state‐and‐transition models (STMs). These models generate forecasts of forest condition that are widely used for forest management planning. Common techniques for evaluating such models are complex, requiring specialized skills not available to non‐modellers. Consequently, model transparency can be limited, hampering collaborative resource modelling that otherwise may increase the chances of management success. 2. We demonstrate evaluation of STMs through network visualization that produces intuitively accessible results, comparable to results of more commonly applied, complex techniques. To evaluate this approach, we statistically test model similarities with empirical data. As examples, we use STMs of forest change, alternately parameterized with information from experts and literature, and compare them to our empirical reference information. 3. Graph theoretical analyses revealed differences in structure and dynamics between alternate STMs. For example, compared to empirical STMs, expert STMs were less complex while literature STMs were more complex. Overall, expert STMs were less similar to empirical STMs than were literature STMs, suggesting information in the expert STMs may deviate more strongly from empirical reference data. 4. We used several techniques that provided complementary information, which produced a comprehensive view of network similarity. We speculate that differences between expert and empirical STMs result from lower complexity of mental models compared to empirical data. While we illustrated our approach using a simple matrix model, it could be adapted for more complex STMs. Improvements of the proposed approach could involve representation of forest change rates with waiting times depicted by multigraphs. 5. Synthesis and applications. Common evaluations of forest change STMs involve complex techniques not easily accessible to non‐modellers. Approaching such models as networks makes their evaluation and statistical testing intuitively accessible to many audiences. Benefits of this approach to modellers include improved communication about models with non‐modellers, while benefits to stakeholders and decision makers include enhanced understanding of models. This may aid a collaborative resource modelling process and should improve the chances of successful resource management plan implementation. While we used an example from boreal forests, our approach could be applied to many other vegetation types globally.
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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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".