The effects of nutrition and density on growth, foliage biomass, and growth efficiency of high-density fire-origin lodgepole pine in central British Columbia
Bibliographic record
Abstract
A factorial thinning and fertilization experiment was established in central British Columbia in a 36-year-old high-density fire-origin lodgepole pine (Pinus contorta Dougl. ex Loud. var. latifolia Engelm.) stand to examine the effects of density, nutrition, and their interaction on tree and stand growth, foliage biomass per hectare, and growth efficiency. Volume growth was increased from 2 to 7 m3·ha1·year1 when fertilizer was applied without thinning and to 5 m3·ha1·year1 when fertilizer was applied with thinning. Thinning increased tree-level foliage biomass and growth efficiency by concentrating limited resources onto fewer trees, resulting in increased tree-level volume growth. Stand-level volume growth was reduced by thinning because of the large reduction in stocking. However, by year 4, stand-level volume growth was the same on control and thinned plots, suggesting that thinned trees have already recaptured the site potential. Fertilization increased both tree-level and stand-level productivity through increases in resource availability per tree and per hectare. This resulted in increased foliage biomass and growth efficiency at the tree and stand level. The combination of thinning and fertilization resulted in the greatest tree-level growth because of increased tree-level foliage biomass and growth efficiency. Boron appears to be the most limiting element followed by sulfur and nitrogen.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".