Modeling percent stocking changes for lodgepole pine stands in Alberta
Bibliographic record
Abstract
A percent stocking change model was developed for lodgepole pine ( Pinus contorta Dougl. ex Loud. var. latifolia Engelm.) in Alberta based on spatially mapped permanent sample plot data. Percent stocking was defined as the percentage of 10 m2 subplots occupied by at least one tree with a minimum height of 1.3 m. The difference equation technique was employed to fit the model. Three model forms were examined and the logistic function was chosen as the final model. Site index was found to be a significant predictor and incorporated into the model. Analyses revealed that the model had correlated, but homoskedastic errors and the correlated errors were modeled by spherical covariance structure using NLINMIX macro in SAS. A percent stocking index, defined as the percent stocking at 50 years total age, was introduced and derived from the developed model. The percent stocking model had both forward and backward projection capabilities. It was demonstrated, both on model fitting and validation data, that the model adequately portrayed the percent stocking dynamics of lodgepole pine stands in Alberta. The model also provided an important basis for creating linkages between reforestation survey results and future yield, which is crucial for sustainable forest management.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".