Bibliographic record
Abstract
The search for a stand density that maximizes total volume growth has continued since the beginning of forestry without producing a definite answer. One of the reasons is that the effect of density on growth is not always separated from those of tree size and age. Such a separation is not easy when the relationship between density and growth is expressed as a graph (Langsaeter's curve). This study develops a simple model that accounts for each main growth predictor individually. It allows one to calculate the density that maximizes volume growth at any given moment (current annual increment of volume). Just as the maximum Reineke's index, this density optimum does not change with age. Fitting the model to long-term data confirms the obvious: because a complete crown closure intercepts more light than a broken canopy, the densest stands produce maximum volume growth. Thus, the current optimal density is equal to maximum density. What is less obvious is that maximum current stand volume growth does not sum up to maximum stand volume. In addition to density, stand volume depends on average tree size, which is larger in less dense stands. The high current density that maximizes volume growth also minimizes diameter increment, which eventually reduces average diameter and stand volume. When density is kept at a stationary (over time) level by thinning, maximum volume is produced by stands with substantially lower density than the current optimum. even higher volume can probably be obtained when density changes with age. The challenge is to find this optimum path of density.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".