What factors should be accounted for when developing a generalized taper function for black wattle trees?
Bibliographic record
Abstract
Taper functions have been widely used for various purposes. Several functions were developed and successfully applied; however, most of these functions fail to account for the influence of stand-level and individual-tree effects of variation on the stem profile. Hence, we aimed in this study to assess how these factors influence the stem profile of black wattle (Acacia mearnsii De Wild.) trees in southern Brazil. There is a notable necessity for developing a domestic market for black wattle solid wood. The database was composed of 218 black wattle trees at age 10 years distributed across the state of Rio Grande do Sul, Brazil. A dimensionally compatible taper equation combined with the mixed-effect modeling approach was used. Additionally, auxiliary variables were included to build a generalized taper function that explains stem form variations. In general, all variables showed a significant influence on the stem profile, except the crown ratio. The inclusion of relative spacing and tree hierarchical position in the taper function resulted in higher accuracy when estimating stem diameters and total tree volume. This study indicates that accounting for attributes at the stand and individual-tree levels may improve stem profile predictions, as well as the biological soundness of the taper function.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".