Variation in mass and nutrient concentration of leaf litter across years and sites in a northern hardwood forest
Bibliographic record
Abstract
Leaf litterfall represents an important nutrient flux in forests, but separating leaves by species and collecting fresh litter annually for nutrient analysis is time-consuming and expensive. To quantify the sources of variation in litterfall nutrient estimates and guide optimal allocation of research effort, we analyzed nutrient concentration (5 years) and mass (6 years) of leaf litter for nine tree species in 13 northern hardwood sites. Coefficients of variation (CVs) in nutrient concentration were higher across sites than over time within sites for most elements; phosphorus was especially variable across sites (56% CV). Thus, to estimate litterfall nutrient fluxes accurately in forests of this type, nutrient analyses should be site-specific as well as species-specific but may not need to be repeated annually (CVs over time averaged 17% for calcium, 21% for magnesium, 28% for potassium, and 32% for phosphorus concentration). Total leaf litterfall mass varied considerably from year to year, ranging from 234 to 370 g·m –2 averaged over 13 sites. We recommend that litter collectors be elevated above the ground to avoid oversampling during extreme wind events. Use of species-specific allometric equations, or even basal area, to estimate the species composition of total litter mass may obviate the need to sort litter by species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 teacher head, 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".