Above- and below-ground biomass accumulation, production, and distribution of sweetgum and loblolly pine grown with irrigation and fertilization
Bibliographic record
Abstract
Increased forest productivity has been obtained by improving resource availability through water and nutrient amendments. However, more stress-tolerant species that have robust site requirements do not respond consistently to irrigation. An important factor contributing to robust site requirements may be the distribution of biomass belowground, yet available information is limited. We examined the accumulation and distribution of above- and below-ground biomass in sweetgum ( Liquidambar styraciflua L.) and loblolly pine ( Pinus taeda L.) stands receiving irrigation and fertilization. Mean annual aboveground production after 4 years ranged from 2.4 to 5.1 Mg·ha–1·year–1for sweetgum and from 5.0 to 6.9 Mg·ha–1·year–1for pine. Sweetgum responded positively to irrigation and fertilization with an additive response to irrigation + fertilization. Pine only responded to fertilization. Sweetgum root mass fraction (RMF) increased with fertilization at 2 years and decreased with fertilization at 4 years. There were no detectable treatment differences in loblolly pine RMF. Development explained from 67% to 98% of variation in shoot versus root allometry for ephemeral and perennial tissues, fertilization explained no more than 5% of the variation in for either species, and irrigation did not explain any. We conclude that shifts in allocation from roots to shoots do not explain nutrient-induced growth stimulations.
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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) | 0.000 | 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".