Regeneration imputation models for complex stands of southeastern British Columbia
Bibliographic record
Abstract
Two imputation techniques for predicting natural regeneration in complex stands prevalent in southeastern British Columbia (BC) were compared using data from the Interior Cedar-Hemlock moist warm subzone variant 2 (ICHmw2) in the vicinity of Nelson, BC. Imputation approaches offer advantages over other modeling approaches in that they provide estimates of many variables at one time (multivariate) and there are no assumptions regarding the probability distributions of the variables to be predicted. For the tabular imputation, the average regeneration per ha was calculated for each combination of five site groups, two residual density classes, five time-since-disturbance intervals, species, and height classes. For Most Similar Neighbour (MSN) imputation, data with both regeneration information, and overstory trees and site information (called reference plots) were used to impute regeneration of plots with only overstory trees and site information (called target plots), by selecting the most similar plot. Of the two approaches studied, the MSN approach gave better results than tabular imputation. The tabular imputation approach is simpler to implement, since tables of results can be published and made available for use. However, the MSN software has been made freely available, resulting in greater ease of access. Key words: multi-species, multi-cohort, nonparametric imputation, multivariate prediction, regeneration estimation
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".