Use of a boundary line approach to determine optimum nutrition levels for three conifers and their application to nutrient diagnosis in the boreal forest
Bibliographic record
Abstract
Knowledge of optimum concentrations and ratios of major nutrients in tree foliage is required to assess the nutrient status of the boreal forest. This thesis aimed at determining foliar nutritional standards for white spruce (Picea glauca (Moench) Voss), black spruce (Picea mariana Mill. B.S.P.) and jack pine (Pinus banksiana Lamb.) and to assess the nutrient status of forest stands across Canada for all major nutrients (N, P, K, Ca, Mg and Mn) using critical values (CVA) and compositional nutrient diagnosis (CND). Trees were sampled at three locations in Ontario and Quebec to cover a gradient of soil fertility levels. A boundary line approach was used in combination with quadratic regression models to assess the relationship between growth and foliar nutrient concentrations or CND scores when free of the effects of interacting environmental factors. (Abstract shortened by UMI.)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 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".