Feature selection by genetic algorithm in nonlinear taper model
Bibliographic record
Abstract
Tree stem profile results from a complex structure of shapes and dimensions determined by ecological processes within the forest. However, the feature selection in the development of taper models has been underinvestigated to date. We propose a genetic algorithm (GA) to assess factors that affect the stem taper and volume of Eucalyptus urograndis trees at different ages (2, 7, and 14 years) in Brazil. A total of 213 sample trees were measured in diameter and height along the stem, crown width, crown base height, crown length, and crown ratio. These variables and the stand age were supplied to the GA that selects variables, replacing those of Kozak’s 2004 model. The performance of models was evaluated using error statistics and residual plots. The GA model was efficient in predicting diameters and volumes, mainly by increasing the accuracy of the estimates in the extreme portions of the trees. This was attributed to the selection of morphometric variables as predictors of stem taper and volume, making them understandable in ecological terms. We highlight GA as a robust tool, since it incorporated the morphometric variables in Kozak’s model that contribute to the estimates.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".