Long-term taper and growth reductions following pruning intensity treatments in giant sequoia (<i>Sequoiadendron giganteum</i>)
Bibliographic record
Abstract
A high degree of stem taper limits the potential value of giant sequoia (Sequoiadendron giganteum (Lindl.) J. Buchholz) when grown for timber. I measured the effects of pruning to different height targets (0, 2.0, 3.5, and 5.5 m), resulting in the removal of 25%–85% of crown length, on the growth of 360 S. giganteum trees over 17 years. Height growth and growth of diameter at breast height were both negatively related to pruning intensity to a similar degree. Within the 17 years following pruning, repeated measurements of stem growth at different heights above the ground revealed that radial stem growth reductions were localized on the stems from which branches were removed. The higher up that trees were pruned, the more that stem radial growth was reduced compared with control trees, with the greatest reductions occurring at stem bases. The effects of pruning decreased over time, but there were still significant differences after 17 years. Girard form class was only influenced by the most intense pruning at a stem height of 5.5 m. A benefit of pruning was reduced taper, but this came at the cost of reduced stem growth. Pruning in S. giganteum must be done carefully with respect to its timing, intensity, and interactions with other management treatments on young stands.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".