Fertilization increased leaf water use efficiency and growth of <i>Pinus taeda</i> subjected to five years of throughfall reduction
Bibliographic record
Abstract
High productivity of fertilized loblolly pine (Pinus taeda L.) plantations in the southern United States is related to increased leaf area index (LAI), but higher evaporative leaf surface area may increase drought vulnerability. To determine if the benefits of fertilization are affected by water availability or the effects of drought are exacerbated by fertilization, the interactive effects of throughfall treatment (ambient throughfall versus throughfall reduction) and fertilization treatment (no fertilization versus one-time fertilization) on a loblolly pine plantation were examined over five growing seasons. Enhancement of LAI and growth from fertilization was unaffected by throughfall treatment, and reductions in LAI, tree height, and stand volume increment in response to throughfall reduction were unaffected by fertilization treatment. Leaf-level stomatal conductance (gS) was decreased and water use efficiency was increased by fertilization and by throughfall reduction. Lower gS was associated with decreased leaf predawn water potential in response to throughfall reduction. In contrast, lower gs in response to fertilization was associated with a reduction in the hydraulic allometry index, a measure of the ability of sapwood to supply water to leaves. These results suggest that fertilization may enhance LAI and growth even under mild or moderate drought.
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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".