A biogeochemical analysis of the Pocono till barrens and adjacent hardwood forest underlain by Wisconsinan and Illinoian till in northeastern Pennsylvania
Bibliographic record
Abstract
We assessed soil and vegetation nutrient capital in the landscape mosaic of till barrens and hardwood forests on the Pocono Plateau in northeastern Pennsylvania. These shrublands, which contain an unusual abundance of rare species, occur primarily on Illinoian-aged glacial till, though some patches grow on Wisconsinan till. We hypothesized that barrens soil and vegetation contain smaller quantities of nutrients than forest soil and vegetation, and under the same vegetation, Illinoian till soils have a smaller nutrient content than Wisconsinan till soils. We measured pH, total C and N, and exchangeable Ca, Mg, K, and Al content of the soils and determined C, N, Ca, Mg, K, and P content of the vegetation. Litter and soil organic matter in the barrens have a higher C/N ratio than the forest. The Illinoian barrens Oa horizon is thicker and contains a greater quantity of exchangeable mineral nutrients than the other Oa horizons. Differences in vegetation nutrient capital strongly mirror differences in biomass. Our results show no strong association of parent material with soil or vegetation nutrient capital. Instead, they suggest that plant community characteristics, not soil nutrient availability, shape the landscape pattern of barrens and forest, particularly plant-driven positive feedbacks primarily involving fire frequency.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".