Spatial stratification in forest modelling
Bibliographic record
Abstract
This paper discusses the concept of spatial stratification (SS) as applied to forest modelling in general, and spatial forest modelling in particular. SS is a way of providing a geographically explicit forest description in forest modelling, or a way of accommodating spatially explicit management objectives and interventions. In the former, called a priori SS, stands of a forest landscape are spatially aggregated into a set of stand clusters which become input to forest modelling. The latter, called dynamic SS, utilizes stands as the input forest description upon which various spatial aggregations occur throughout forest modelling. Distinctions between the two alternative approaches are highlighted and implementation considerations are examined within the forest landscape management design context. The paper concludes that: (i) modelling techniques are directly linked to forest stratification approaches; and (ii) a priori stratification is seriously limited in spatial modelling for landscape management design where multiple and often conflicting spatial objectives exist. In view of these findings, the paper outlines an alternative spatial forest modelling approach using a combination of dynamic SS and heuristic optimization. Key words: spatial stratification, spatial forest modelling, heuristic optimization, forest management
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".