Fourteen-year growth response of young lodgepole pine to repeated fertilization
Bibliographic record
Abstract
Four rates of N (0, 50, 100, and 150 kg·ha1) with and without a fertilizer mix containing P, K, S, Ca, Mg, and micronutrients were applied to a stand of 8-year-old lodgepole pine (Pinus contorta var. latifolia Engelm.) in four applications during 7 years. Fourteen years after the first fertilization, mean tree basal area and volume were significantly increased in the treatments containing the two highest rates of N plus the fertilizer mix. Mean tree basal area was 38% greater and mean tree volume was 42% greater in the highest N (525 kg·ha1) plus mix treatment than in the control treatment. Stand basal area increased 48% over the control in the highest N plus mix treatment. Mean tree basal area and volume were 27 and 25% greater than the control under the intermediate N rate (350 kg·ha1) with mix, respectively. Height growth was not significantly increased by fertilization. Both higher rates of N (four applications of >100 kg·ha1) and the fertilizer mix were required for significant growth responses to occur. Addition of N alone or mix alone had a negative effect on mean tree basal area and volume. Addition of higher N rates without the mix decreased foliar total S and SO4-S concentration and increased the N/S ratio. Addition of the higher rates of N without the mix aggravated slight K deficiencies. The responses to the higher N rates in combination with the fertilizer mix are mainly attributable to S.
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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 | 0.001 |
| 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".