A dendrochronological analysis of black spruce productivity in wetlands and adjacent uplands of Nova Scotia, Canada
Bibliographic record
Abstract
and Adjacent Uplands by Georgia Konstantinidis Coniferous forest uplands and wetlands are abundant in Nova Scotia.Tree growth in wetlands is known to be stunted compared to uplands.The objective of this study was to compare the growth of black spruce trees in wetlands and uplands of four Nova Scotia sites.Along a transect at each site, tree cores were taken from selected black spruce trees, for which tree height and diameter at breast height (DBH) was also measured.Data on peat moss and soil moisture were collected to determine whether trees were in wetland or upland.Black spruce age and tree ring productivity were assessed by analyzing tree cores with Windendro software.The average width of the outermost ten tree rings of each tree core was used as a measure of recent growth and productivity.Black spruce age and growth were relatively consistent across all habitats.Spruce radial growth was not always greater in upland environments, but trees were taller in uplands than wetlands at two of the study sites.Favourable environmental factors for tree growth often resulted in taller trees in upland habitats because the soils are drier.More recent tree growth appears to be indifferent to soil moisture on forested wetland landscapes; I presume it is because of unmeasured effects such as climate change and competition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".