Assessing prediction errors of generalized tree biomass and volume equations for the boreal forest region of west-central Canada
Bibliographic record
Abstract
Aboveground tree biomass and volume are required inputs to models that estimate carbon budgets and ecosystem productivity. Generalized equations are often used to estimate biomass and volume when local equations are unavailable. This study determined whether there was a concomitant increase in prediction error from increasing levels of equation generalization. Local site, generalized regional, and generalized national allometric equations were compared for 10 species distributed across 119 sites in a region defined by west-central Canada. This study employed regression fit statistics and two prediction error metrics, the average prediction error (APE) from the prediction error sum of squares (PRESS) statistic and mean prediction bias. The APE was 9, 12, and 25 kg of biomass per tree for local site, generalized regional, and national equations, respectively. The mean prediction bias for biomass and volume were statistically similar between local level and generalized regional equations across all species. Predictions from generalized national equations were statistically similar for 5 of 10 species when compared with those from local site and generalized regional equations. While local site equations were most accurate for a given site, results indicate that generalized regional equations will produce reasonable estimates of biomass and volume at sites in this region of Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".