Contrasting Litter Nutrient and Metal Inputs and Soil Chemistry among Five Common Eastern North American Tree Species
Bibliographic record
Abstract
Forest composition has been altered throughout Eastern North America, and changes in species dominance may alter nutrient cycling patterns, influencing nutrient availability and distribution in soils. To assess whether nutrients and metals in litterfall and soil differed among sites influenced by five common Ontario tree species (balsam fir (Abies balsamea (L.) Mill.), eastern hemlock (Tsuga canadensis (L.) Carr.), white pine (Pinus strobus L.), sugar maple (Acer saccharum Marsh.), and yellow birch (Betula alleghaniensis Britt.)), litterfall and soil chemistry were measured at a managed forest in Central Ontario, Canada. Carbon (C) and macronutrient (nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg)) inputs in litterfall varied significantly among sites, primarily due to differences in litterfall mass, which was greatest in deciduous-dominated sites, while differences in elemental concentrations played relatively minor roles. Trace metal inputs in litterfall also varied, with much higher zinc (Zn) and cadmium (Cd) in litterfall within yellow birch dominated stands. Mineral soil oxide composition was very similar among sites, suggesting that differences in soil chemistry were influenced by forest composition rather than parent material. Litter in deciduous-dominated stands had lower C/N, and soils were less acidic than conifer-dominated sites. Deciduous stands also had much shorter elemental residence times in the organic horizons, especially for base cations (Ca, Mg, K) compared with conifer-dominated sites, although total soil nutrient pools were relatively consistent among sites. A change from stands with greater conifer abundance to mixed hardwoods has likely led to more rapid cycling of elements in forests, particularly for base cations. These differences are apparent at small scales (100 m2) in mixed forests that characterize many forested regions in Eastern North America and elsewhere.
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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 | 0.001 |
| 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) | 0.000 | 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".