Combining Area-Based and Individual Tree Metrics for Improving Merchantable and Non-Merchantable Wood Volume Estimates in Coastal Douglas-Fir Forests
Bibliographic record
Abstract
Forest management practices can increase climate change mitigation potential through applications focused on carbon budgets. One such application involves utilizing non-merchantable material (i.e., logging residues typically piled and burned) for bio-energy. However, limited remote sensing data is available for estimating wood residues until after timber has been harvested, at which point recovery of residual wood is of little financial interest. This research utilizes a hybrid method to develop models that provide pre-harvest estimates of the amount of merchantable and non-merchantable material that would result from harvesting and investigates the scalability and transferability of such measures to the harvest block level. Models were trained using 38 plots across two sites dominated by Douglas-fir, then expanded to ten harvest blocks, and transferred to eight blocks from two sites without training data before being compared against multiple independent block-level estimates. Model results showed root mean square errors of 35% and 38% for merchantable and non-merchantable volumes, respectively. Merchantable volume estimates in blocks with training had average absolute differences from the harvest scale (9–34%) similar to transferred blocks without training (15–20%). Non-merchantable model results were also similar in both trained and transferred harvest blocks, with the pre-harvest model results having lower differences from the post-harvest geospatial versus field surveys. The results from this study show promise for hybrid methods to improve estimates of merchantable wood volume compared to conventional forest cover data approaches, and provide the ability to predict non-merchantable volumes within the range of accuracy of post-harvest residue survey methods.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".