Development of a method to determine tree species nutritional standards from natural variation in tree growth and leaf chemistry
Bibliographic record
Abstract
Optimum nutritional levels for most commercial hardwoods of eastern Canada are unknown. This thesis dealt with the development of a method to determine nutritional standards using within site variation in tree growth and foliar chemistry. To this end, sugar maple (n = 87) and red maple (n = 39) trees were sampled in summer 2001 at the Station de biologie des Laurentides. Leaves were sampled for nutrients (N, P, K, Ca, Mg and Mn) and tree stems were measured for determination of basal area growth (BAG). Similar measurements for trees sampled annually during 1995--2001 were also used to measure the effect of annual variation on nutritional standards. A boundary line approach was used to assess tree growth response to nutrition using nutrient concentrations and Compositional Nutrient Diagnosis (CND) scores as predicting variables. A Basal Area Growth Index (BAGI) was computed using the live crown ratio to correct for the effect of stand density on BAG. An iterative and unbiased protocol was also developed to eliminate outliers. Optimum, critical and optimal range levels were derived from quadratic models significant at P < 0.15.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".