Humus buildup in boreal forests: effects of litter fall and its N concentration
Bibliographic record
Abstract
This synthesis paper presents a model for estimating the buildup of soil organic matter in boreal deciduous and coniferous forests. A basic model was developed using data from a well-studied Scots pine (Pinus sylvestris L.) forest (SWECON site) and based on limit values for litter decomposition and amounts of litter fall. A local validation gave a calculated humus accumulation that differed by 8% from the amount measured in the stand. This model was further validated using data for humus accumulated for 2984, 2081, and 1106 years, predicting an accumulation close to the measured amount, and for needle litter the missing fractions were 16, 17, and 6%, respectively, for the three groups. The limit value for litter decomposition is negatively related to the litter's initial N concentration; thus, N-rich litter should have a larger resistant fraction left than N poor. This relationship was validated using nine paired stands of monocultures: eight pairs of Scots pine and Norway spruce (Picea abies (L.) Karst.) and one pair of red alder (Alnus rubra Bong.) and Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco). The measured amount of SOM was related to foliar litter fall and its N concentration. In all cases the more N-rich litter gave in all cases the more N-rich Norway spruce litter gave a significantly higher accumulation of humus for Norway spruce in spite of a higher litter fall for Scots pine. Also, red alder gave more SOM than Douglas-fir and in an expected relation to the litter N concentration. A consequence of this would be that C sinks of different efficiencies or capacities would tend to accumulate SOM at different rates.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".