Height Growth Models for Western Larch in British Columbia
Bibliographic record
Abstract
Abstract British Columbia's foresters currently use height growth curves developed with data from Montana to estimate the height and productivity of western larch (Larix occidentalis). The ability of the presently used curves to accurately predict the height growth of British Columbia's larch population is unknown. The production of new curves with local data could improve our ability to predict heights and allow increasingly precise yield projections in British Columbia. Data from 105 western larch stem analysis plots were collected from across the natural range of larch in British Columbia. The measured plots were naturally established, fire-origin, even-aged, and exhibited no indications of suppression or disease. A Richards function was fit to the data from each plot and used to generate height-age and site index information. Four models were fit to the plot data: conditioned logistic, Chapman Richards, conditioned Chapman Richards, and conditioned Weibull. The Chapman Richards model had the best fit to the data, although all four models had similar fit statistics. Overall, the Chapman Richards model is slightly more accurate at estimating heights than the currently used model. West.J. Appl. For. 17(2):66–74.
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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 | 0.002 |
| 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) | 0.003 | 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".