Total and epiphytic litter under the canopy of <i>Acer macrophyllum</i> in an old-growth temperate rainforest, Washington State, USA
Bibliographic record
Abstract
The amounts and ecological importance of epiphytic litterfall has often been overlooked in forest ecosystem studies. However, epiphytes participate in whole-ecosystem dynamics by capturing and retaining nutrients from atmospheric sources and transferring these nutrients to other ecosystem components. We quantified epiphytic litterfall under the canopy of mature bigleaf maples (Acer macrophyllum Pursh) and compared it with other litter components in an old-growth temperate rainforest in Washington State. Total litterfall during one year was 4760 kg·ha−1, with the greatest contribution from bigleaf maple leaves. Of the total litter input, 546 kg·ha−1 consisted of epiphytic litter, equivalent to 12% of total fine litter input, the highest contribution of epiphyte litterfall documented for this type of forest. Compared with other studies in the Pacific Northwest, our estimations of conifer needle inputs relate to the location of the littertraps. Bigleaf maple leaves dominated carbon (C) and nitrogen (N) returns in litter; epiphytic litterfall contributed 240 kg·ha−1·year−1 of C (∼11% of total C inputs) and 5.7 kg·ha−1·year−1 of N (∼11% of total N inputs) to the forest floor. Inputs of N from epiphytes and bigleaf maple litter under the canopy of this tree could be important in augmenting N in this old-growth ecosystem.
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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".