Cost implications of cluster plot design choices for precise estimation of forest attributes in landscapes and forests of varying heterogeneity
Bibliographic record
Abstract
Tradeoffs occur when deciding between improving forest inventory precision by increasing sample size or by augmenting cluster plot design factors like size or subplot separation distance. The nature of these tradeoffs changes with variation in type and scale of the spatial pattern of the attribute of interest. To understand the impacts of relationships between type and scale of spatial heterogeneity and cluster plot design efficiency, we constructed a factorial simulation experiment and analysed relationships among forest inventory cost, cluster plot design factors, and different spatial heterogeneity scenarios constructed via simulation. To calculate cost, we constructed a cost model that accounted for both on- and between-plot costs. We found that type and scale of heterogeneity have important implications for plot design choices. Homogeneous stands and landscapes are the least costly to inventory. Subplot area and count have stronger impacts than subplot separation on cost efficiency, particularly in landscapes with aggregated forest patterns and in stands with homogeneous tree patterns. We discuss results in the context of the physical interaction between cluster plot geometry and spatial patterns at different scales, provide computer code for simulations, and suggest principles that forest inventory cluster plot design specialists should consider when designing inventories.
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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 | 0.023 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".