An examination of species-specific growing space utilization
Bibliographic record
Abstract
A comparison is made among the size–density relationships of sugar maple ( Acer saccharum Marsh.), European beech ( Fagus sylvatica L.), Norway spruce ( Picea abies (L.) Karst), lodgepole pine ( Pinus contorta Dougl. ex Loud. var. latifolia Engelm.), eastern white pine ( Pinus strobus L.), Scots pine ( Pinus sylvestris L.), loblolly pine ( Pinus taeda L.), and Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco var. menziesii ). Species-specific minimum density of full site occupancy (NMCA) and average maximum density (NAMax) equations were developed using forest inventory data and existing models. NMCA and NAMax estimates were used to compare species growing space utilization over a range of stand diameters (12–50 cm). Results suggest growing space utilization varies among the evaluated species and that species differences may change over the diameter range evaluated. Analysis also highlights that a growing space usage gradient is present across species. One end of the gradient is occupied by species that maintain higher densities at small sizes and exhibit greater rates of density decline as mean diameter increases. In contrast, other species maintain lower densities at small diameter, but have lesser rates of density reduction as diameter increases. Finally, results highlight the importance of using species-specific models when quantifying size–density relationships and developing stand-density management regimes.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".