Sizedensity metrics, leaf area, and productivity in eastern white pine
Bibliographic record
Abstract
Sizedensity metrics are used extensively for silvicultural planning; however, they operate on biological assumptions that remain relatively untested. Using data from 12 even-aged stands of eastern white pine (Pinus strobus L.) growing in southern New Hampshire, we compared size-density metrics with stand productivity and its biological components, including leaf area index (LAI) and measures of crown morphology. Density indices included Reineke's stand density index (SDI), a 3/2 relative density law, and trees per hectare. We examined models with and without site index and stand age as components, to predict total stand accretion (PAI), LAI, and growth efficiency (GE). LAI was a strong linear predictor of PAI (R2 = 0.89). However, of the indices tested only SDI was a significant predictor of accretion, and none were significantly related to LAI or GE. Site index was not a significant predictor of any variable when used alone, but in combination with SDI and stand age did lead to significant relationships with PAI (R2 = 0.84), LAI (R2 = 0.67), and GE (R2 = 0.92). Of the density indices tested only trees per hectare was strongly correlated with crown attributes. These results demonstrate that size-density metrics combined with other stand attributes are reasonably correlated with biological measures of stand growth.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".