Evolution, history, and use of stem taper equations: a review of their development, application, and implementation
Bibliographic record
Abstract
Stem taper equations, which predict the change in stem form from ground to tip, have become the primary means for estimating bole volume. Stem taper equations can provide predictions with similar levels of accuracy as volume equations, but with greater flexibility, a wider range of potential uses, and consistency between taper and volume. This review is a synthesis of the current state of knowledge on stem taper equations and an assessment of challenges for future model refinement. It includes the history and evolution of stem taper model forms, which have received tremendous attention and focus over the last several decades. Additional focal areas covered are (i) the use of additional covariates beyond tree diameter at breast height (DBH) and total height; (ii) alternative statistical methods for developing stem taper equations such as parametric, semiparametric, and nonparametric approaches; (iii) key considerations for proper development, application, and use of stem taper equations such as sample size requirements, local calibration, and evaluation; and (iv) a synthesis of key findings, future opportunities, and ongoing challenges. Current and developing technologies such as terrestrial laser scanning (TLS) offer an unprecedented opportunity to measure stem form in much greater detail at significantly lower costs and time requirements than traditional methods. Overall, continued development, refinement, and application of stem taper equations will remain important given the critical nature of tree volume for science, accurate inventories, and ultimately, sustainable forest management.
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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".