Regeneration of black ash (<i>Fraxinus nigra</i> Marsh.) in hardwood swamps of the Great Lakes – St. Lawrence Forest Region
Bibliographic record
Abstract
An inductive, ordination-based approach was used to explore patterns in the microenvironment and natural regeneration of black ash across a range of representative stands in the central Ontario portion of the Great Lakes -St. Lawrence Forest Region (GLSL) near Lake Nipissing, Ontario, Canada. The objective of this study was to describe patterns of regeneration of black ash and determine the associations between multiple indicators of black ash regeneration success and biotic and abiotic factors. Using a randomized sampling design with specific selection criteria, 12 black ash stands were sampled, each with three 400 m2 circular sample plots that contained three 4 m2 sub-plots. A suite of environmental variables such as elevation, topographic wetness index, slope, and soil characteristics (percent moisture, pH, total nitrogen, exchangeable phosphorus, potassium, magnesium) were determined for each stand. Variables capturing regeneration potential, including density, diameter, and height of all germinants, seedlings, saplings and sprouts were also collected. A sample of 15 dominant or co-dominant trees in each stand, as well as numerous black ash seedlings, saplings and sprouts were examined using dendrochronological methods to estimate ages. Principal component analysis ordinations were performed exploring the variation in environment and black ash regeneration variables among the stands. High soil moisture and the presence of canopy gaps, (as indicated by high standard deviation of canopy closure), were key variables associated with greater abundance of regeneration. Black ash was the dominant species in all stands, which were also generally characterized by a common reverse-J diameter distribution. It was observed that black ash in the sapling layer reached substantial ages (up to 60 years), indicating they are capable of withstanding long periods of suppression below the canopy. Collectively, these findings suggest that black ash-dominated stands in the study area are regenerating in multiple cohorts. Based on these patterns, it appears that regeneration in these stands is occurring through gap-phase mechanisms, suggesting single tree selection as the best management option for black ash in the GLSL.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 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".