Eight-year responses of light interception, effective leaf area index, and stemwood production in fertilized stands of interior Douglas-fir (<i>Pseudotsuga menziesii</i> var. <i>glauca</i>)
Bibliographic record
Abstract
Data from 72 Douglas-fir (Pseudotsuga menziesii var. glauca (Beissn.) Franco) plots across the Interior Northwest were used to determine whether (i) increased intercepted photosynthetically active radiation (%IPAR), effective leaf area index (LAIe), and stemwood volume production (cubic metres stemwood per hectare per year) could still be detected 7 or 8 years after nitrogen fertilization and (ii) fertilization would increase the efficiency by which light is converted into stemwood volume. Projected LAIe varied from 1.82 to 6.07 m2·m-2 on the control plots. The fertilized plots intercepted 9-11% more light than the control plots (P < 0.001); they also had 22-25% higher LAIe than the control plots (P < 0.001). Stemwood volume production increased by 25-29% relative to the control (P < 0.001) and increased exponentially with %IPAR across all study plots (R2 = 0.57). Stemwood growth efficiency averaged 12.0 ± 0.4 (mean ± SE), 13.5 ± 0.4, and 13.7 ± 0.4 m3·ha-1·a-1 per IPAR for the control, low fertilization, and high fertilization plots, respectively (P < 0.01). Fertilization thus induced increases in both light interception and the efficiency with which intercepted light was converted to stemwood across the region.
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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".