Effects of height imputation strategies on stand volume estimation
Bibliographic record
Abstract
Subsampling and subsequent imputation of tree heights can improve the predictive performance of stand volume estimation but may also introduce biases. Using coastal Douglas-fir data from southwest Oregon, USA, the predictive performance of several height imputation strategies for estimating stand volume was evaluated. A subsample of 1–15 trees was randomly selected per stand, and missing heights were imputed using a regional Chapman–Richards function with diameter only and diameter plus stand density measures, fitted using a nonlinear least-squares model (NFEM) and a nonlinear mixed-effects model (NMEM). Missing heights were imputed using the regional height–diameter equation and by adjusting the equation with a correction factor (NFEM) or with predicted random effects (NMEM) to calibrate the height–diameter relationship to each stand. Differences in actual stand volumes, calculated with measured heights, and predicted stand volumes, calculated using measured heights for the subsampled trees and predicted heights for those with missing heights, were used to compare the alternative height imputation methods. Precision and bias were poorest for the regional models, especially NMEM, and best for the adjusted models also using NMEM. Results suggest that a similar subsample of heights (n = 4) is required for precise stand volume estimation as has been reported for height.
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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 | 0.152 | 0.267 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".