Commercial thinning and nitrogen fertilization increases merchantability in 68-year-old lodgepole pine: 20-year results
Bibliographic record
Abstract
A commercial thinning and fertilization experiment using 2 × 6 factorial design was initiated in a 68-year-old lodgepole pine stand in Alberta. Commercial thinning to remove 50% basal area from below was combined with nitrogen fertilization at five levels (no fertilizer, 200 kg/ha N Urea + boron, 200 kg/ha N + blend, 400 kg/ha N + boron, 400 kg/ha N + blend, and 400 kg/ha N ammonium nitrate + boron). This study reports results from re-measurement 20 years later. At the stand level, commercial thinning had no impact on the final stand volume but did increase the cumulative merchantable volume (volume removed at time of thinning + final standing volume). Individual diameter at breast height (DBH) growth was increased by thinning and fertilization treatments individually and additively meaning that individual tree growth was greatest for trees that were both thinned and had high fertilization (400 levels). Individual tree diameter at thinning was the best single predictor of 20-year growth response with medium-sized trees responding to thinning alone and thinning and 400-level fertilization. Mortality was increased by fertilization on unthinned plots while thinning increased the proportion of large sawlogs (>20 cm DBH) by 20%. Overall, commercial thinning and fertilization can be used to increase merchantability in natural lodgepole pine stands, even during later rotation.
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 | 0.000 | 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".