Soil-site relationships for young white spruce plantations in north central Ontario / Richard R. LaValley
Bibliographic record
Abstract
Site index of white spruce (Picea glauca (Moench) Voss) in North \nCentral Ontario was related to features of soil and topography using both \nmultiple regression techniques and principal component analysis. Two \ndifferent measures of site index, TOTSI25 (site index is total height at \ntotal age of 25 years) , and BHSI15 (breast height site index is total \nheight at 15 years breast-height age) were used as dependent variables; 81 \nsoil and topographic values were independent variables considered for \nanalysis. Preliminary regressions computed from 54 plots indicated poor \nrelationships between TOTSI25 and soil and topographic variables. \nPreliminary regressions also indicated that the correlations were much \nstronger using BHSI15. Correlations also were much stronger when the plots \nwere stratified into three landform types as opposed to unstratified \nregressions. Three final regression equations were based on the \nrelationship between BHSI15 and lacustrine, morainal, and glaciofluvial \nlandform groups, and explained 77, 73, and 65 percent of the variation in \nBHSI15, respectively. The final regression equation for the lacustrine \nlandform included the type of clay deposit (CLAY) , the depth to a root \nrestricting layer (DRRL), and the hue of the C horizon (HUEC). The final \nregression equation for the morainal landform included the natural \nlogarithm of the depth to a root restricting layer (LNDRRL), and the pH of \nthe C horizon (PHC). The final regression equation for the glaciofluvial \nlandform included the drainage class of the soil (DRAIN). The ability of \nthe final regression equations to predict BHSI15 was tested on 14 \nindependent test plots; these tests showed close agreement between actual \nsite index based on stem analysis and site index predicted from the \nregression equations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 teacher head, 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".