Long-term evolution of composition and structure after repeated group selection over eight decades
Bibliographic record
Abstract
In northeastern North America, group selection is frequently used in northern hardwood forests to maintain uneven-aged stand structure and promote regeneration of tree species spanning a range of shade tolerances. For this study, long-term application of group selection at the Bartlett Experimental Forest, New Hampshire, USA, provided a unique opportunity to address cohort- and stand-level progression after 80 years of treatment. Cohort-level evolution reflected successional and developmental dynamics associated with even-aged forest systems, whereas aggregate stand-level conditions were consistent with expectations for uneven-aged systems. As cohorts aged, diameter distributions progressed towards descending monotonic forms and species composition transitioned from shade-intolerant species to shade-tolerant species. Standing deadwood and downed woody material in cohorts followed trajectories of aging even-aged stands through time. Although American beech (Fagus grandifolia Ehrh.) was a primary species across cohorts and at the stand level, stand-level regeneration included a mixture of ecologically and commercially valuable species. These long-term results offer important insights into emergent cohort- and stand-level conditions and processes that may affect continued recruitment of desirable compositional and structural conditions in stands managed using group selection over numerous cutting cycles.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".