Implementing a PrognosisBC Regeneration Submodel for the Complex Stands of Southeastern and Central British Columbia
Bibliographic record
Abstract
PrognosisBC is a growth and yield computer model, which is an adaptation of the USDA Forest Vegetation Simulator (FVS). To date, the model has been calibrated by the Ministry of Forests, for use in a number of Biogeoclimatic (BEC) subzones in the Nelson, Cariboo and Kamloops forest regions. The architecture of the model is such that it is applicable to a wide range of timber types and stand conditions. Natural regeneration is a key component in projecting the dynamics of uneven-aged, mixed species complex stands. However, due to ecological differences, the original Northern Idaho regeneration submodel of FVS was not accurate for use in Southeastern BC. As a result, the current PrognosisBC user policy limits the number of stand entries and the duration of projections following a disturbance. The specific objectives of this research project were: • design a protocol for using imputation techniques to simulate natural regeneration in PrognosisBC; • create the necessary databases (from existing regeneration data) to implement a Nearest Neighbour imputation method in predicting regeneration; and • develop the programming and linkages to PrognosisBC to create a prototype regeneration component. In collaboration with the forest industry in the Cariboo, Kamloops, and Nelson forest regions and
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".