Early height growth and regeneration : applicability of prognosis components to the southern interior of British Columbia
Bibliographic record
Abstract
Predicting stand structure through time is a challenge in all aspects of forest management. Predictions in multi-cohort, mixed species, or1 spatially varied stands have mostly been based on field experience and have not been clearly quantified. The main hypothesis of this thesis is that the regeneration and small tree height growth components of the Northern Idaho variant of the growth and yield model Prognosis, can be calibrated for use in the stands of the southern interior of BC. The original equation forms of Prognosis Nl were applied to data collected in stands of the Columbia - Shuswap moist warm Interior Cedar - Hemlock variant of the Interior Cedar Hemlock moist warm subzone (ICHmw2 of the Biogeoclimatic Ecosystem Classification system of BC) in the vicinity of Nelson, BC. The same forms were then re-fitted with the Nelson data, and finally, other model forms were applied to Nelson data. In all cases, the original fitted equations in Prognosis Nl were outperformed by either the refitted equations or equations with Nelson-based variables. The equations presented in this thesis are a valid start to the calibration of regeneration and small tree height growth models Prognosis80. Some issues need to be addressed for model improvements. Prognosis Nl was not developed in BC and it uses a different ecosystem classification. Although correspondences have been made between BC site series and Idaho habitat types, the two systems are different, and so are the sites. These differences contribute to errors in model predictions. The data set used for developing Prognosis Nl was much larger than the data collected around Nelson. Some data categories used in the Prognosis model had more predictor variables than the number of observations in the corresponding data category in the Nelson data set. This lack of data resulted in non-robust models. :|" Despite these issues, the equations resulting from this calibration process improve the estimates of small tree height growth and regeneration in multi-cohort or mixed species stands in the southern interior of BC. Prior to the calibration efforts of Prognosis60, no quantitative tools were in place to aid silviculturists for predictions in these stands. Although these predictions are not completely accurate, they can serve as guidelines, to supplement field experience, for making predictions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".