How forest models are connected to reality: evaluation criteria for their use in decision support
Bibliographic record
Abstract
Choice of a model for exploring forest management options depends on the decision space defined by the actions, indicators, ecosystem scope, and cybernetic context of the decisions. To be useful in a particular decision context, candidate models must include all relevant hypotheses of effects of the actions on the indicators in a spatial and temporal structure appropriate for the particular decision. The architecture of a suitable model is implied or constrained by these components of the decision space. A set of attributes for assessing a model's suitability for decision support is proposed. In addition to a firm foundation in science, decision support models should provide predictions with quantified bias and precision, and without artifacts that influence choice of management alternatives. Descriptions of information flow across levels of integration within and between models and between models and field observations should be included in model descriptions. Schematic diagrams of these flows illustrate several broad classes of how modelling systems may be linked to reality to improve their utility.
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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 | 0.140 | 0.477 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".