Soil-site relations for jack pine (Pinus banksiana Lamb.) in Northeastern Ontario
Bibliographic record
Abstract
Site index of jack pine {Pinus banksiana Lamb) measured on 76 plots in \nnortheastern Ontario was related to features of soil and topography using \nmultiple regression. Site index at breast height age 50 years (SIBHSO) was used \nas the dependent variable, and 119 soil and topographic values were used as \nindependent variables. Regression equations were imprecise using all 76 plots. \nWhen separate equations were computed for bedrock-moraine, glaciofluvial, and \nmoraine landforms, precision was much greater with R2 values of 0.78, 0.51 and \n0.60, respectively. \nThe final bedrock-moraine equation consisted of slope percent, thickness \nof the B horizon, and percent stones in the top 25 cm of the soil profile. The \nglaciofluvial equation consisted of depth to average rooting, depth to moisture \nrestricting layer, and percent silt in the B horizon. The moraine equation \nconsisted of depth to maximum rooting, pore pattern, percent sand and percent \nsilt In the BC horizon. \nThe northeastern Ontario plots were combined with Schmidt and \nCarmean's (1988) 131 plots. New regressions based on the pooled data had R2 \nvalues of 0.84, 0.55, 0.37, 0.57, and 0.24 for bedrock-glaciofluvial, bedrock-moraine, \nglaciofluvial, lacustrine, and moraine landforms, respectively. These \nanalyses produced valid jack pine soil-site equations for the combined bedrock \nand lacustrine landforms in northeastern and north central Ontario; but \nequations combining data for glaciofluvial and moraine landforms were \nimprecise. \nThe northeastern and north central Ontario plots were pooled with 16 \nplots from northwestern Ontario. New regressions based on the pooled data \nhad R2 values of 0.22, 0.47, and 0.17 for glaciofluvial, lacustrine, and moraine \nlandforms, respectively. These analyses resulted in equations that had \nunacceptably low precision. The failure to compute acceptable soil-site \nequations was attributed to different soil and topographic variables influencing \nthe height growth of jack pine in northwestern Ontario.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".