A non-asymptotic sigmoid growth curve for top height growth in forest stands
Bibliographic record
Abstract
Since the height horizon remains undetected in the vast majority of height series sampled in forest stands, even of notable ages, the realism of the traditional asymptotic-size modelling assumption is questioned. The aims of the study were to present an original non-asymptotic growth model and to test its accuracy against asymptotic-size equations. The equation proposed is a first-order four-parameter autonomous differential equation. The related sigmoid size curve has a parabolic branch of time. It was tested on 349 old growth series of top height (1047 stem analyses) selected to explore the maximum observed ranges of age and site conditions in seven temperate tree species growing in pure and even-aged stands. The fitting accuracy of this equation and three classical asymptotic-size growth equations (Richards, Hossfeld IV and Korf equations) were compared, with parameterizations of increasing flexibility. For the different parameterizations, the proposed growth equation showed higher performances than asymptotic growth equations, attributed to its non-asymptotic property and to the mathematical independence between parameters related to the inflection point and late growth. Top height growth was therefore accurately modelled by a sigmoid curve not based on the asymptotic-size assumption. This equation may be of general relevance to tree growth modelling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".