Dendrochemistry and growth of three hardwoods in three geological regions of southern Quebec from 1940-1999
Bibliographic record
Abstract
This thesis used novel methodologies in dendrochemistry to observe past nutrient and Al change in relation to incremental stem xylem growth to predict current and future forest health. The methods included (1) sequential digestion of wood tissue to remove the elemental fraction that is mobile across tree rings leaving the structurally intrinsic, residual (or less mobile) ion fraction for analysis and (2) transformation of elemental concentrations into multivariate ratios (compositional nutrient diagnosis (CND)) over a time series. Sampling of trees represented a gradient in acidity resilience using three regions of southern Quebec (St. Lawrence Lowlands; Lower Laurentians; and Appalachian Highlands) and three species (red maple ( Acer rubrum L.); sugar maple (Acer sacharum Marsh.); and American beech (Fagus grandifolia Ehrh.). The elemental residual fraction had differences from the mobile fraction over time for Ca, Mg and Mn, but not for K or Al. The base rich Saint-Lawrence region had the highest and slightly increasing incremental stem xylem Al of the regions yet had stable or increasing growth. By contrast the acid sensitive Appalachian region had the greatest increase in Al accompanied by a decrease in growth beyond 1970. The Appalachians also had the highest Mn, which had an adverse effect on growth of sugar maple. The acid-resilient species American beech had stable or decreasing Al while having stable or increasing growth in contrast to the less resilient sugar and red maple. The nutrient poor Laurentian region had a persistent deficiency of K over time but no relationships with Al. Aluminium had a general negative correlation with the other canons. Although Mn had the highest levels in red maple for each region, it appears to be limiting growth. The changes in wood chemistry and growth over time appear to be driven by the resilience of the region or species to increasing acidic load in the ecosystems.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".