Prediction of standing tree defect proportion using logistic regression and ordered decision thresholds
Bibliographic record
Abstract
In forest inventories, it is often of interest to calculate amounts of usable wood volume in trees. This usually requires knowledge of how much of the total volume is unusable (cull) due to form or decay deficiencies. This information is primarily obtained when collecting data on sample plots, although the assessments are often difficult and subjective. To provide an alternative, methods were developed to estimate individual-tree cull attributes. The procedure initially involves classification using logistic regression to assign trees to one of three categories (no cull, intermediate cull, entirely cull) based on probability cut points. Subsequently, trees classified as having intermediate cull are assigned a cull amount predicted from a generalized linear regression model. The best results for cull prediction were obtained using cut points that minimized absolute prediction error; however, better prediction of net cubic volume of trees was realized when the errors were weighted by tree size. The model-based approach may be particularly useful in obtaining temporal consistency, such that trend estimates may better reflect the actual change in forest resources.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".