Nutrient concentrations and nitrogen mineralization in forest floors of single species conifer plantations in coastal British Columbia
Bibliographic record
Abstract
We examined the extent to which nutrient concentrations and C and N mineralization rates in forests floors under different tree species are predictable from the chemistry of foliar litter and its rate of decomposition. We studied replicated single species plantations of western redcedar (Thuja plicata Donn ex D. Don), western hemlock (Tsuga heterophylla (Raf.) Sarg.), Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), and Sitka spruce (Picea sitchensis (Bong.) Carr.) at four locations. Nutrient concentrations in forest floors correlated poorly with litter nutrient concentrations; the only significant relationships were for Ca and K. Nitrogen mineralization correlated weakly with forest floor C/N ratio, and differed more among sites than among species. None of the litter chemistry parameters were related to net N mineralization rates. Decomposition was fastest in hemlock litter, intermediate in Douglas-fir litter and lowest in cedar litter. Litter also decomposed more rapidly on hemlock forest floors than on cedar forest floors. Rates of N mineralization in the forest floors were not related to rates of decomposition of foliar litter. Differences among sites in N mineralization rates were related to the understory vegetation composition, particularly the amount of the ericaceous shrub salal, which in turn was related to slope position. These site factors appeared to override the effect of tree species on rates of N mineralization.
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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 | 0.000 |
| 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".