Spatial patterns of forest floor properties and litterfall amounts associated with bigleaf maple in conifer forest of southwestern British Columbia
Bibliographic record
Abstract
This study was aimed at detecting the spatial characteristics of forest floor properties and litterfall amounts related to bigleaf maple (Acer macrophyllum Pursh) within conifer forest. Two 36-m × 36-m plots, centered on individual dominant bigleaf maple stems, were sampled at 129 systematic locations and tested for forest floor pH, cation exchange capacity, exchangeable cations, and mineralizable N. Tree stem location, forest floor horizon depths and litterfall amounts were measured. The kriging approach was used to visualize overall spatial patterns, Moran's I was used to give a global measure of spatial autocorrelation over the sampled region, and local indicators of spatial association (LISA) was used to detect and locate significant spatial clustering of similar values at the local scale. Most soil chemical properties had higher values in locations adjacent to the bigleaf maple stem, up to distances of 2.5 m from the stem on both study plots, and all exchangeable cations were positively spatially autocorrelated (P < 0.05) up to distances of 4 m. The majority of bigleaf maple litter (84% on plot 1, 92% on plot 2) was found to be deposited directly beneath the canopy extent. This study provides an understanding of the underlying spatial patterns of bigleaf maple influence on soil properties at plot scale. Key words: Bigleaf maple, spatial analysis, broadleaf species, plant-soil interactions local indicators of spatial association, forest floor
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".