Soil-site relations for jack pine in the Thunder Bay area / by Margaret G. Schmidt. --
Bibliographic record
Abstract
Site index of natural jack pine (Pinus banksiana Lamb.) in the Thunder Bay area was related to features of soil and topography using multiple regression analyses. Site index (SI, height of jack pine trees at 50 years from breast height) was used as the dependent variable and 129 soil and topographic variables were considered for the analyses. Due to \nproblems associated with numerous independent variables, multicollinearity, and highly variable soil groups, the four preliminary regression equations computed from 95 plots could not be accepted as valid equations. The final analyses limited the number of independent variables considered for analyses and landform types were more precisely defined. The "best" \nfinal regression equations explain 83, 65, 65, and 75 percent of the variation in SI for bedrock, morainal, glaciofluvial, and lacustrine landforms, respectively. The variables included in the bedrock equation are depth to bedrock (DBR), and coarse fragment content of the A horizon \n(CFRAGA). The variables included in the moraine equation are depth to a restricting layer (DRL), percent clay in the A horizon (CLA), and coarse fragment content of the C horizon (CFRAGC). The variables included in the glaciofluvial equation are depth to a moist restricting layer (DMRL), and percent slope (SLOPE). The variables included in the lacustrine \nequation are thickness of the A horizon (THA), and pH of the BC horizon (PHBC). The final regression equations for bedrock, moraine, and glaciofluvial sites predict SI reasonably well for 15 check plots.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.005 |
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; both teacher heads agree on what is shown here.
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".