Modeling mass and nitrogen remaining in litterbags for Canadian forest and climate conditions
Bibliographic record
Abstract
A new Forest Litter Decomposition Model (FLDM) is presented to simulate mass, N and carbon/nitrogen ratios (C/N) according to the 1992–1998 leaf litterbag data of the Canadian Intersite Decomposition Experiment (CIDET). This experiment involved 10 litter types, with litterbags placed on the ground of 18 upland and 3 wetland sites across Canada. The calibrated model based on first-order reaction kinetics calculates total mass, N concentration and C/N for each litter type and location using: three compartments (fast, slow, and very slow), four parameters for compartment initialization; three for compartment-based decay; three to assess the climate influence on decay; and one each to determine the rate o f N-mineralization and the final C/N ratio. With FLDM, the initial fast fraction is determined from the initial water-extractable and acid-hydrolyzable or acid-unhydrolyzable portions of the litter; the initial ash content determines the ratio between the slow and very slow fractions. Mean July and January air temperatures, and annual precipitation, determine the climate effect on litter decay. Initial N concentration and the upland-wetland difference determine the N-mineralization coefficient. Model performance was fairly consistent by litter type, location, and year; r2 ranged from 0.83 to 0.97, from 0.64 to 0.92 and from 0.54 to 0.93 for mass remaining, N concentration and C/N, respectively. Key words: Forest litterbags, organic matter decomposition, N concentrations, climate, litter type, CIDET
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".