Long-term compositional dynamics of Acadian mixedwood stands under different silvicultural regimes
Bibliographic record
Abstract
Pathways of forest compositional dynamics over five decades (1953–2005) were reconstructed using measurements from permanent sample plots in a long-term silviculture experiment in a central Maine mixedwood forest. The objective of this study was to elucidate the dynamics of tree species composition at the sample plot level in relation to the initial composition when the experiment was established (1953–1957) and harvest disturbance history following a wide range of even-aged and uneven-aged silvicultural treatments. Cluster analysis revealed three groupings of sample plots based on pretreatment composition and harvest disturbance history, or nine subclusters (i.e., three harvest disturbance histories nested within each pretreatment composition). From 1953 to 2005, the silvicultural treatments generated an array of compositional outcomes at the plot level. Hardwood dominance increased following a history of heavy and infrequent harvests, while northern conifer dominance was maintained where harvests were lighter and more frequent. The importance of balsam fir ( Abies balsamea (L.) Mill.) changed little across a range of harvest intensities. A ubiquitous decline in northern white-cedar ( Thuja occidentalis L.) was found among silvicultural treatments, suggesting that additional intervention may be needed to promote cedar recruitment. Plot-level compositional dynamics indicated that neighborhood-scale stand dynamics were associated with variability in harvest disturbance overlain on plot-to-plot variability in tree species composition at the time the experiment was established.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".