Imputing missing height measures using a mixed-effects modeling strategy
Bibliographic record
Abstract
This paper proposes a method whereby heightdiameter regression from an inventory can be incorporated into a height imputation algorithm. Point-level subsampling is often employed in forest inventory for efficiency. Some trees will be measured for diameter and species, while others will be measured for height and 10-year increment. Predictions of these missing measures would be useful for estimating volume and growth, respectively, so they are often imputed. We present and compare three imputation strategies: using a published model, using a localized version of a published model, and using best linear unbiased predictions from a mixed-effects model. The bases of our comparison are four-fold: minimum fitted root mean squared error and minimum predicted root mean squared error under a 2000-fold cross-validation for tree-level height and volume imputations. In each case the mixed-effects model proved superior. This result implies that substantial environmental variation existed in the heightdiameter relationship for our data and that its representation in the model by means of random effects was profitable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".