Rates of litter decomposition over 6 years in Canadian forests: influence of litter quality and climate
Bibliographic record
Abstract
The effects of litter quality and climate on decomposition rates of plant tissues were examined using percent mass remaining (MR) data of 10 foliar litter types and 1 wood type during 6 years exposure at 18 upland forest sites across Canada. Litter-quality variables used included initial nutrient contents (N, P, S, K, Ca, Mg) and carbon fractions (determined by proximate analysis and 13 C nuclear magnetic resonance spectroscopy). Climate variables used included mean annual temperature; total, summer, and winter precipitation; and potential evaptranspiration. A single-exponential decay model with intercept was fit using the natural logarithm of 0- to 6-year percent MR data (LNMR) for all 198 type by site combinations. Model fit was good for most sites and types (r 2 = 0.640.98), although poorest for cold sites with low-quality materials. Multiple regression of model slope (K f ) and intercept (A) terms demonstrated the importance of temperature, summer precipitation, and the acid-unhydrolyzable residue to N ratio (AUR/N) (r 2 = 0.65) for K f , and winter precipitation and several litter-quality variables including AUR/N for A (r 2 = 0.60). Comparison of observed versus predicted LNMR for the best overall combined models were good (r 2 = 0.750.80), although showed some bias, likely because of other site- and type-specific factors as predictions using 198 equations accounted for more variance (r 2 = 0.95) and showed no bias.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".