The role of permanent site factors in the assessment of soil treatment effects: A case study with a site preparation trial in jack pine plantations on glacial outwashes
Bibliographic record
Abstract
In silvicultural trials, the confounding influence of permanent soil properties is assumed to be minimal. A covariance analysis using total elemental concentrations of parent material (geochemistry) and soil particle size distribution (texture) was used to understand the role that these variables could play in tree growth and foliar nutrient status, and in the growth response to site preparation of 16- to18-yr-old jack pine plantations growing on seemingly homogeneous glacial outwash deposits. Three treatments were tested in a replicated design: direct plantation, and site preparation with TTS or with Bräcke. The combination of particle size distribution and soil geochemistry explained the site quality index (SQI) in control plots (R2 = 0.94) better than soil texture alone. In all plots, SQI was strongly related to K and Mg concentrations in foliage and in humus and, to a lesser degree, to foliar N concentrations. A covariance analysis using soil texture and geochemical composition indicated that while site preparation had an effect on tree growth, the sites that responded the most to site preparation were the ones with the lowest growth potential as determined by soil texture and geochemistry. Key words: Silviculture, site preparation, site quality index, growth, permanent soil factors, texture, geochemistry, nutrition
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".