Adjustment of the age–height relationship for uneven-aged black spruce stands
Bibliographic record
Abstract
Site index (SI) is commonly used in natural stands, even when their diameter distribution deviates from that of the monospecific, even-aged, fully stocked stands used to develop basic age–height relationships. Since deviations from basic age–height trajectories can be reflected in deviations of stand diameter distribution from a bell shape, we incorporated different diameter diversity indices into an age–height equation to help improve height predictions and determine which index is best related to stand dominant height. This procedure was performed using black spruce ( Picea mariana (Mill.) BSP) stands from a large network of permanent sample plots established across the province of Quebec, Canada. The age–height model that minimized the Akaike’s information criterion used the Shannon evenness index (ESh) as an equation modifier accounting for the diameter diversity variable. The model showed that for stands established on relatively poor sites (SI = 9), no substantial differences in dominant height were found between two contrasting ESh values. For SI = 15, however, the larger ESh value increased the dominant height by as much as 1 m at 80 years. These results suggest that introduction of ESh into an age–height model can improve calculation of site index, particularly in regions characterized by the presence of numerous uneven-aged stands.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".