A species-specific, site-sensitive maximum stand density index model for Pacific Northwest conifer forests
Bibliographic record
Abstract
Maximum stand density index (SDIMAX) models were developed for important Pacific Northwest conifers of western Oregon and Washington, USA, based on site and species influences and interactions. Inventory and monitoring data from numerous federal, state, and private forest management groups were obtained throughout the region to ensure a wide coverage of site characteristics. These observations include information on tree size, number, and species composition. The effects and influence on the self-thinning frontier of plot-specific factors such as climate, topography, soils, and geology, as well as species composition, were evaluated based on geographic location using a multistep approach to analysis involving linear quantile mixed models, random forest, and stochastic frontier functions. The self-thinning slope of forest stands dominated by Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) was found to be –1.517 and that of stands dominated by western hemlock (Tsuga heterophylla (Raf.) Sarg.) was found to be –1.461, leading to regionwide modelled SDIMAX values at the 95th percentile of 1728 and 1952 trees per hectare, respectively. The regional model of site-specific SDIMAX will support forest managers in decision-making regarding density management and species selection to more efficiently utilize site resources toward healthy, productive forests.
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".