Modeling self-thinning of unthinned Lake States red pine stands using nonlinear simultaneous differential equations
Bibliographic record
Abstract
Forest management demands thorough knowledge of ecological systems. Tree interaction dynamics are one component of these ecological systems. Developing growth models which incorporate ecological "laws" such as the self-thinning rule can lead to better understanding of the laws, to better understanding of what is still unknown, and to what is in need of refinement. To this end a system of simultaneous differential equations incorporating logical, linked hypotheses regarding growth and mortality is proposed and fit to data from red pine (Pinus resinosa Ait.) plantations in the Lake States (Minnesota, Michigan, and Wisconsin). Using this modeling framework it appears that stand initiation history has a large impact on the level of the self-thinning boundary for red pine growing in the Lake States. Stands with initially high density exhibited lower self-thinning boundaries than stands with lower densities. Site quality (as measured by site index) chiefly affected the rate at which stand dynamics progress. Higher quality sites progressed through stand development at faster rates than did sites with lower quality.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".