Litter decomposition in British Columbia forests: Controlling factors and influences of forestry activities
Bibliographic record
Abstract
Four commonly held beliefs about litter decomposition rates were tested in a suite of field experiments in British Columbia forests: (1) decomposition is slower in cold (northern and high-elevation) forests, (2) decomposition is faster in clearcuts than in forests, (3) broadleaf litter decomposes faster than needle litter, and (4) decomposition is faster in N-fertilized forests. Litter decomposition was slowest in dry biogeoclimatic zones and fastest in wet zones. Overall, it appears that moisture is more limiting than temperature for litter decomposition across British Columbia. The effect of clearcutting on litter decomposition rates varied among forest types. Province-wide, mass loss of pine needle litter was significantly slower in clearcuts than in adjacent forests, but this difference disappeared after 3 years. Aspen leaves and forest floor material decomposed at similar rates in forests and clearcuts. Decomposition of broadleaf litter was slightly faster than needle litter during the first 2 years, but slowed in subsequent years. After 3 years there was no significant difference between the mass remaining for broadleaf and conifer litter. In N-fertilized plots, higher N concentrations did not affect the rate of decay in litter or in forest floors. Many of our beliefs about litter decomposition and influences of forestry practices thereon should be revised in light of new empirical evidence.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".