Manganese dynamics in decomposing needle and leaf litter — a synthesis
Bibliographic record
Abstract
The aim of the present synthesis paper was to determine whether concentration changes and net release of manganese (Mn), as related to accumulated litter mass loss, are related to initial Mn concentration, mean annual temperature (MAT), mean annual precipitation (MAP), and tree genus or species. We also examined whether limit values for decomposition are related to initial litter Mn concentration, MAT, and MAP. We compiled 84 foliar litter decomposition studies, conducted mainly in boreal and temperate forest ecosystems, for which Mn dynamics had been well documented. Manganese concentration and amount were related to accumulated litter mass loss at each sampling time for each single study, as well as for (i) all studies combined (n = 748) and (ii) for species groups viz. Norway spruce (Picea abies (L.) Karst.) (n = 284), pine (Pinus) species (n = 330), and deciduous species (n = 214). The changes in Mn concentration with accumulated mass loss followed quadratic functions showing significantly higher Mn concentrations for Norway spruce vs. Scots pine (Pinus sylvestris L.) (p < 0.0001) and vs. deciduous species (p < 0.01), as well as significantly higher for deciduous species vs. Scots pine (p < 0.0001). Manganese release rates were different among the three species groups (p < 0.001). Still, rates were related to initial Mn concentrations (p < 0.001) for all litter types combined and for the three species groups. Norway spruce released Mn more slowly than pine and deciduous species. Rates were related to climatic factors for litter of Norway spruce and deciduous species. Limit values for all litter and for pine species separately were related to Mn (p < 0.001) and MAT (p < 0.001). For Norway spruce, limit values were related to MAT (p < 0.001) and MAP (p < 0.01). It appears that Norway spruce litter retains Mn more strongly in the litter structure, producing humus richer in Mn than does litter of pine and deciduous species.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".