The influence of bigleaf maple on chemical properties of throughfall, stemflow, and forest floor in coniferous forest in the Pacific Northwest
Bibliographic record
Abstract
It is predicted that bigleaf maple ( Acer macrophyllum Pursh) will almost double in frequency in British Columbia by 2085 due to climate change. We address whether its frequency increase could influence chemical properties of throughfall, stemflow, and forest floor due to species-specific effects. Eight plots with a single bigleaf maple tree in the centre of conifers were paired with eight Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco) plots without bigleaf maple. Compared with conifer plots, bigleaf maple throughfall and stemflow had higher pH and K concentration. The under-canopy and near-trunk forest floor associated with bigleaf maple showed higher pH, total exchangeable bases, cation-exchange capacity, and concentrations of exchangeable Ca and Mg. In addition, the near-trunk forest floor had higher base saturation and concentrations and contents of NO3-N and contents of total N and S. Throughfall and stemflow beneath bigleaf maple appear to contribute to higher pH and N availability in the forest floor. The results suggest that there is a soil microsite around bigleaf maple stems that is influenced by stemflow. These enriched microsites proximal to bigleaf maple trunks would allow bigleaf maple to have legacy effects on soil fertility and promote conifer productivity later in succession following bigleaf maple mortality.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".