Comparing selection system and diameter-limit cutting in uneven-aged northern hardwoods using computer simulation
Bibliographic record
Abstract
Comparisons of selection system and diameter-limit cutting based on trials in specific settings have often yielded conflicting results. We used a simulation approach to evaluate sawtimber production over three cutting cycles on 10 sugar maple ( Acer saccharum Marsh.) dominated plots of varying initial forest structure. Treatments on each plot included light, moderate, and heavy intensities of selection system silviculture and diameter-limit cutting. Harvested sawtimber volumes were initially higher on all plots using diameter-limit cutting, but selection system outperformed diameter-limit cutting at later entries on 7 of the 10 plots. Volume differences between cutting types ranged among plots from 0.3 to 26 m 3 ·ha –1 , equating to a less than 1% to as much as a twofold difference. Average volumes from selection system at later entries were 20%–40% greater than diameter-limit cutting, due in part to consistent production in large sawtimber (≥46 cm). Yields from real stands could vary from these simulations where mortality losses (not modeled here) differ between treatments as a result of competition or logging damage. Findings suggest that cumulative sawtimber volumes from repeated selection system silviculture could eventually surpass that of diameter-limit cutting, but at a rate depending on initial stand conditions and harvesting intensity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".