Constructing a virtual forest: Using hierarchical nearest neighbor imputation to generate simulated tree lists
Bibliographic record
Abstract
A nearest neighbors method for generating simulated tree lists has been developed. The method employs an implicit two-scale hierarchy to incorporate information from a coarse scale representing the distribution of stand attributes across a region and a fine scale representing the distribution of tree attributes within a stand. The tree list generation method was implemented and tested using data from untreated, naturally regenerated and planted forests in western Oregon, western Washington, and southern British Columbia west of the Cascade Mountains. Simulated tree lists were generated from stand scale attributes for each of the actual tree lists in the data. Distributions of stand scale and tree scale attributes were estimated and used to compare the simulated and actual tree lists. At the stand scale, distributions of quadratic mean diameter and average height for the simulated and actual stands were in very good agreement, having approximately 98% of their probability mass in common for each attribute. At the tree scale, comparisons of the distributions of diameter at breast height, height, and species composition between the simulated and actual stands were more variable, with approximately 84% of the simulated stands identified as statistically similar to their respective actual stands.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".