Height Growth Models for Western Larch in British Columbia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it