Bibliographic record
Abstract
Growth and yield predictions for managed even-aged stands in British Columbia are based on TASS, an individual-tree distance-dependent growth model driven by an unusually detailed description of crown development. Because of its complexity, most applications utilize previously generated stand-level yield tables rather than running TASS directly. I have developed a differential equation approximation to the stand-level dynamics predicted by TASS that mimics the aggregate behaviour with sufficient accuracy for many practical purposes. Versions of this model, called TADAM, exist for planted coastal Douglas-fir, lodgepole pine, and white spruce. TADAM can efficiently project stand development starting from any initial conditions, and subject to any combination of thinnings. Its relative simplicity makes it suitable for embedding into landscape-level planning models and other decision support systems. It has been implemented as a C function library, as an interactive simulator running on a PDA, and as an Excel spreadsheet add-in. An example of thinning and planting density optimization is briefly described. Key words: growth and yield, stand dynamics, thinning, optimization, Pinus contorta, Picea glauca
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".