Decomposition vectors: a new approach to estimating woody detritus decomposition dynamics
Bibliographic record
Abstract
A chronosequence of three species of logs (Pinus sylvestris L., Picea abies (L.) Karst, and Betula pendula Roth.) from northwestern Russia was resampled to develop a new method to estimate rates of biomass, volume, and density loss. We call this resampling of a chronosequence the decomposition-vector method, and it represents a hybrid between the chronosequence and time-series approaches. The decomposition-vector method with a 3-year resampling interval gave decomposition rates statistically similar to those of the one-time chronosequence method. This indicated that, for most cases, a negative exponential pattern of biomass, volume, and density loss occurred. In the case of biomass loss of P. sylvestris, however, polynomial regression indicated decomposition rates were initially low, then increased, and then decreased as biomass was lost. This strongly suggests three distinct phases: the first when decomposers colonized the woody detritus, a second period of rapid exponential mass loss, and a third period of slow decomposition. The consequences for this complex pattern of decomposition were explored at the ecosystem level using a simple model. We found that a single rate constant can be used if inputs vary within a factor of 10, but that this approach is problematical if inputs are more variable.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".