Estimating trembling aspen productivity in the boreal transition ecoregion of Saskatchewan using site and soil variables
Bibliographic record
Abstract
Pinno, B. D. and Bélanger, N. 2011. Estimating trembling aspen productivity in the boreal transition ecoregion of Saskatchewan using site and soil variables. Can. J. Soil Sci. 91: 661–669. The productivity of trembling aspen, as expressed by site quality index (SQI), in natural stands growing on three different soil parent material types (fluvial, lacustrine and glacial till) in the boreal transition ecoregion of Saskatchewan was evaluated by using soil and site variables. The soil and site variables used were either general categorical variables, such as parent material and ecosite, or continuous variables, such as soil texture (percent sand or clay), pH, carbon, nitrogen, C:N ratios, and elemental composition. It was not possible to reliably estimate SQI using only categorical site variables or continuous soil variables when all plots were grouped together. However, when plots were grouped by parent material type, over 45% of the variability in trembling aspen productivity was explained using the common soil measurements of texture and pH. In estimating SQI, there was an interaction between both pH and soil texture with parent material. On fluvial and lacustrine parent materials, increased clay content was positively correlated with SQI, but was negatively correlated with SQI on till, while pH was positively correlated with SQI on fluvial parent material, but negatively on lacustrine. Including more sophisticated measures of soil nutrient availability in the forest floor and BC horizons did not improve the SQI prediction. This study indicates that it is possible to estimate trembling aspen productivity using simple site and soil variables, provided that differences in soil properties within parent material groupings are considered in the analysis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".