Overstory litter inputs and nutrient returns in an old-growth temperate forest ecosystem, Olympic National Park, Washington
Bibliographic record
Abstract
Overstory litterfall rates and nutrient returns were determined in an old-growth temperate rainforest watershed in the Hoh River valley, Olympic National Park, Washington. Litter was sorted into green needles, senescent needles, fine wood, reproductive, and miscellaneous litter (mostly arboreal lichens and mosses). Understory and coarse woody debris inputs were not determined. Total annual overstory litterfall averaged 3594 kg·ha1 and varied among the six plant communities in the watershed. There was a trend for litterfall to be higher in the upper watershed; elevations ranged from 180 to 850 m. Needles provided the greatest amount of litterfall (60%) with woody litter and other material averaging 18 and 22%, respectively. Highest senescent needle litterfall occurred from July to October, but highest woody litterfall was from January to April. Green needles provided only 3% of annual needle litterfall. Green and senescent needle litterfall were related to western hemlock (Tsuga heterophylla (Raf.) Sarg.) basal area, suggesting that this shade-tolerant species was the greatest contributor to needle litterfall. The following quantities (kg·ha1) of nutrients were returned to the forest floor annually: Ca, 26.8; N, 24.6; K, 4.0; Mg, 3.0; P, 2.9; Mn, 1.7; and Na, 1.2.
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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".