A dynamic equation for a published Sitka spruce site-dependent height-age model
Bibliographic record
Abstract
We present a new Sitka spruce (Picea sitchensis (Bong.) Carr) site-dependent height-age model that is based on a dynamic site equation simulating previously published height-age curves for various productivity sites. The new model is an improvement over the previous model because it uses any arbitrary height-age pair to directly predict a height at another age, instead of using a fixed base-age site index as the older model does. Consequently, it can also be used directly to compute height at any age from site index or site index from any height and age instead of relying on numerical solutions for site index computations. The model predicts the same heights for any site as the original fixed base-age model and has the same desirable properties of polymorphism, inflection point, variable asymptotes, logical behaviour, theoretical basis, parsimony, and improved extrapolation. The model is offered as an algebraic improvement only, and therefore it was calibrated on pseudo-data generated from the old models predictions rather than on real data. The proposed equation mimics the old model better than the other dynamic equations tested in this study, which is illustrated using examples with the Chapman-Richards function. Analysis with the real data might offer further improvements to the model predictions. Key words: Base-age invariance, height-age model, model properties, nonlinear regression, Sitka spruce, site index
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".