Does forest tree species composition impact modelled soil recovery from acidic deposition?
Bibliographic record
Abstract
Acidic deposition depleted soil base cation pools throughout central Ontario, particularly during the second half of the 20th century. While sulfur (S) and nitrogen (N) deposition have declined in recent decades, forest harvesting may continue to remove base cations from soils, highlighting the need for reliable soil chemistry forecasts. This study investigated whether differences in soil chemistry among forest plots dominated by different tree species affected predictions using a dynamic biogeochemical model, the Very Simple Dynamic model. Soil base saturation was modelled from 1850 to 2100 in plots dominated by balsam fir (Abies balsamea (L.) Mill.), eastern hemlock (Tsuga canadensis (L.) Carr.), white pine (Pinus strobus L.), sugar maple (Acer saccharum Marsh.), or yellow birch (Betula alleghaniensis Britt.). Three scenarios that manipulated future atmospheric S and N deposition and forest harvesting (2020–2100) were applied. When future atmospheric S and N deposition remained at 2020 levels and harvesting continued, base saturation increased marginally (2.0%–4.5%) in all plots. Further increases in base saturation were minor (∼1%) by 2100 when deposition reductions were implemented. When future forest harvesting was excluded, soil base saturation increased 3.4%–8.5% from 2020 to 2100. These results suggest that tree species composition has minimal influence on modelled soil chemistry forecasts in response to changes in acidic deposition, and such models can be broadly applied for regional predictions.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".