Soil and pine foliage nutrient responses 15 years after competing-vegetation control and their correlation with growth for 13 loblolly pine plantations in the southern United States
Bibliographic record
Abstract
Influences of competition-control treatments on long-term soil and foliar nutrition were examined using a regional data set (the Competition Omission Monitoring Project) that documents loblolly pine (Pinus taeda L.) plantation development for 15 years after early intensive woody and (or) herbaceous control. Examined were trends for macro nutrients in soils sampled at years 0 and 15 and in pine foliage at years 2, 6, and 15 and their correlations with one another and with pine growth. Early control treatments resulted in distinct plantation successional patterns with contrasting herbaceous and woody components, all under pine-dominated canopies. There was an overall decrease in soil nutrient concentrations after 15 years of pine-plantation management, while C, N, and Ca decreased most after vegetation control. Early herbaceous treatments resulted in significantly less foliar N and K at year 15 as well. Foliar nutrient contents and fascicle mass at year 2 tended to be better correlated with year-15 pine volume than values at year 6 or year 15. Year-15 P concentrations had the strongest correlations between soil and foliar nutrient levels (r = 0.71–0.77). By year 15, intensive pine culture and vegetation control had placed demands on soil nutrient supplies to support enhanced growth that have not yet been replaced.
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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".