A shift in the gap dynamics of <i>Betula alleghaniensis</i> in response to single-tree selection
Bibliographic record
Abstract
We investigated yellow birch ( Betula alleghaniensis Britt.) growth patterns and disturbance frequency before and after the advent of selection harvesting at the Ford Forestry Center in the Upper Peninsula of Michigan, USA, through the use of tree-ring analysis. Based on the boundary-line release detection procedure, 88% of the trees in our sample (n = 67) displayed evidence of at least one moderate or major release. Prior to active forest management, releases were infrequent, and trees that originated during that period had growth histories consistent with establishment after large-scale disturbances (i.e., large canopy gaps, >200 m2). Conversely, tree cohorts that recruited to the canopy more recently displayed a growth pattern suggestive of periodic small gap expansion. Given the declining representation of yellow birch in these forests, the latter strategy, although probably sufficient to prevent extirpation, is unlikely to ensure a sustainable and harvestable population of this and other midtolerants in managed uneven-aged forests. Our results highlight the importance of considering the cumulative influence of infrequent disturbances and chance events on the maintenance of tree species diversity.
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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 | 0.000 | 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) | 0.001 | 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".