Stand density index in uneven-aged ponderosa pine stands
Bibliographic record
Abstract
Stand density index (SDI) was developed to quantify relative stand density in even-aged stands. Application of SDI in uneven-aged stands has been described mathematically but not justified biologically. Diameter-class trends in SDI and sapwood area across 14 uneven-aged ponderosa pine (Pinus ponderosa Dougl. ex P. & C. Laws.) stands in eastern Montana were examined to elucidate the biological underpinnings of the SDI summation method. Results indicate that the SDI summation method is biased in its apportionment of relative stand density across diameter classes in uneven-aged ponderosa pine stands. SDI apportions greater relative density to small trees than to larger ones. Therefore, SDI may overpredict site occupancy for reverse J-shaped diameter distributions with more small trees than large ones, and it may underpredict occupancy with nonreverse J-shaped diameter distributions. Application of the SDI summation method in uneven-aged ponderosa pine stands may be biologically justified only if site occupancy diameter-class trends are taken into account when interpreting SDI values. Replacing the self-thinning scaling factor of the SDI summation method with more biologically relevant scaling relationships may create improved relative density measures for 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".