Three methods for avoiding the impacts of incompatible site index and height prediction models demonstrated on jack pine curves for Ontario
Bibliographic record
Abstract
Fixed base-age site indices are commonly used as a covariate in height prediction models, whereby separate site index prediction equations are used with measured age and height to predict the site index when it is unknown. In such systems, a bias may result in the height prediction if the site index equation is incompatible with the height equation. We demonstrated such bias using as an example recently published models for jack pine in northern Ontario with incompatible site index and height equations. Then we offered solutions that reduce the bias in height predictions assuming that the primary objective was to predict height. First, we re-estimated the site index equation parameters using both the site index and height equations as a common prediction system and holding the published height equation parameters constant while minimizing errors in height predictions. This substantially reduced the incompatibility between the site index and the height equations. Second, we demonstrated the use of two dynamic equations as alternatives to the fixed base-age equations. Even using an irrelevant dynamic equation for another species substantially reduced the bias in short-term jack pine height projections. However, the dynamic equation fit to the jack pine height model was the most effective in reducing the bias for height projections relative to all other considered solutions and produced the least biased, most parsimonious, and most flexible solution. Key words: incompatible site index and height equations, fixed base age equations, dynamic equations
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".