Understory succession and the gap regeneration cycle in a <i>Tsuga canadensis</i> forest
Bibliographic record
Abstract
We examined understory succession in current and former canopy gaps in mature Tsuga canadensis (L.) Carrière forests in southeastern Ohio. First, we reconstructed understory succession in current gaps by sampling 28 gaps ranging from 0 to 9 years. Second, we reconstructed the gap history of a single Tsuga community by clustering release events evident in the growth rings of 156 trees. The two reconstructions formed an 80-year chronosequence, allowing us to examine both short-term effects of gaps as well as long-term effects of closed-canopy conditions on eight common understory species. Understory cover was highest in canopy gaps. All eight understory species in the study exhibited higher cover in canopy gaps than beneath the closed Tsuga canopy. In addition, one species increased percent biomass allocated towards shoots. Although most species increased cover in gaps, different species reached peak cover at different times during gap succession. Understory species reaching peak cover early in the life of the gap were also present beneath the closed canopy and invested primarily in lateral biomass. Understory species reaching peak cover late in the life of the gap, however, were confined to gaps and invested primarily in vertical biomass. Understory cover declined during gap closure; this decline was most pronounced 20 years following gap formation. Thereafter, total understory cover increased slightly, although never to gap levels.
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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) | 0.000 | 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".