Spatial patterns of soil and vegetation in a 40-year-old slash pine (<i>Pinus elliottii</i>) forest in the Coastal Plain of South Carolina, U.S.A.
Bibliographic record
Abstract
A study was conducted at the Savannah River Site in a 40-year-old slash pine (Pinus elliottii Engelm.) plantation in South Carolina to characterize the spatial patterns of soil, forest floor, and plant community variables and to investigate correlations among the variables. Spatial soil and litter samples were collected on five 0.25-ha plots. The spatial patterns of the variables were characterized by global variance, autocorrelation range, and patchiness. The cross-variable relationships were explored using Pearson's correlation tests to examine functional heterogeneity (i.e., to determine if structural heterogeneity reflected ecological processes). Variances of the variables calculated without regard to spatial position (i.e., global variance) were generally low. Average range of spatial autocorrelation was about 58 m for the forest floor variables, 11 m for soil variables, 11 m for basal area of large pines (>30 cm diameter at breast height), and less than 11 m for basal area of smaller pines or other woody species. Few strong spatial correlations among the forest floor and soil variables were observed. Spatial patterns of pines and hardwoods were weakly correlated with litter quality patterns and soil nitrogen. We conclude that the sample plots were generally homogeneous and that differences in soil resource levels were probably too small to influence spatial pattern of vegetation in this 40-year-old plantation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".