Stand dynamics and structure of old-growth <i>Fraxinus nigra</i> stands in northern Minnesota, USA
Bibliographic record
Abstract
Black ash ( Fraxinus nigra Marsh.) forests of north-central North America are currently threatened by the non-native emerald ash borer ( Agrilus planipennis, EAB). Despite the wide distribution of F. nigra ecosystems, and the concern over EAB impact, little is known about their structure and natural stand dynamics. We sampled six old-growth F. nigra stands to assess structure, composition, tree recruitment, and past disturbance. Dendrochronological results revealed that disturbance rates fluctuated markedly over the past 200 years or more, but remained relatively low, suggesting small- to moderate-scale disturbances. Recruitment age structures revealed that ( i) F. nigra is able to maintain long-term dominance through extended longevity as a canopy tree, and ( ii) these systems have fairly continuous recruitment over time, with most sites showing F. nigra recruitment in every decade in the chronology. We speculate that recruitment is coupled with water table fluctuations, in addition to canopy disturbance, as these stands are subject to both frequent flooding and effective soil drought (given the shallow root systems). The low rates of past canopy disturbance and associated gap-phase replacement by F. nigra highlight the potential for dramatic shifts in these systems following emerald ash borer invasion and subsequent canopy tree mortality.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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 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".