Decomposition of broadleaf and needle litter in forests of British Columbia: influences of litter type, forest type, and litter mixtures
Bibliographic record
Abstract
We measured rates of decomposition at three sites representing the major mixedwood forest types of British Columbia: (i) boreal forests of white spruce (Picea glauca (Moench) Voss) and trembling aspen (Populus tremuloides Michx.); (ii) coastal forests of Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) and red alder (Alnus rubra Bong.); and (iii) a wet interior forest of Douglas-fir, paper birch (Betula papyrifera Marsh.), and lodgepole pine (Pinus contorta Doug. ex Loud.). Mass loss of litter of each species (both pure and in combination with the other species) was measured for 2-5 years in forests of each species to determine (i) if broadleaf litter decomposed faster than needle litter, (ii) if litter decomposed faster in broadleaf or mixedwood forests than in coniferous forests, and (iii) if mixing with broadleaf hastened decomposition of needle litter. The broadleaf litters decomposed faster than needles during the first year but, thereafter, decomposed more slowly, so differences were small after 3 years. Litter tended to decompose faster in the broadleaf forests than in the coniferous forests. There was either no effect or a slight suppression of decomposition when litters were mixed; thus, there was no evidence that addition of broadleaf litter hastened decomposition of needle litter. The results clearly indicate that the mixing of needle litter with broadleaf litter is unlikely to hasten decomposition in mixedwood forests of British Columbia. The main influence of broadleaves was more rapid decomposition in broadleaf or mixedwood forest floors, which does not appear to be simply an effect of litter quality or litter mixing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".