Bibliographic record
Abstract
Abstract. A whole-stand survival model is presented, that is parsimonious and well-behaved when extrapolated, making it particularly useful in data-poor situations. It is argued, on biological and systemtheoretical grounds, that a suitable differential equation for the mortality rate should contain number of trees and top height on the right-hand side, avoiding age, mean diameter, or basal area. Following Eichhorn’s hypothesis, site quality can be neglected by modelling rates relative to height growth. proposed model is dN/dH = −aN b H c,whereN is number of trees per unit area, H is top height, and a, b and c are parameters to be estimated. The equation can be integrated to predict mortality between any two points in time. Satisfactory performance is demonstrated with a white spruce data set from British Columbia. It is shown that the model generalizes concepts of relative spacing, and mortality models for radiata pine and Douglas-fir used by Beekhuis in New Zealand in the 1960’s. Asymptotic behaviour is related to the 3/2, Reineke, and relative spacing self-thinning laws. Limitations of the self-thinning theories and relationships among their various forms are discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".