Bibliographic record
Abstract
Use of Prince Edward Island’s wood supply model requires the foundation of close portrayal of species –stand type growth in the Province as an integral part of achieving accurate predictions. To assist such portrayal a reassessment of Provincial Plantation Yield curves was accomplished using visual comparison with Permanent Sample Plot data. This paper denotes the method involved and details results and recommendations. Background In 1990 the Prince Edward Island (PEI) volume to age yield curves for commercial tree species were constructed using data from permanent sample plots (PSP’s). The PSP’s have been measured every three years since 1980. The curves were reassessed in 1994 and 1997 prior to use in the Provincial wood supply model- a Dos computer program called ‘FORMAN’. If they reflected suitable growth patterns they were accepted and if they did not they were revised. This process incorporated comparisons from other provincial yield curves if the local data was insufficient. ‘WOODSTOCK’, which was developed by ‘Remsoft Inc.’ of Fredricton, New Brunswick, is the new wood supply model that will replace FORMAN this year as the Provincial wood supply modelling software. This Windows compatible program has an increased efficiency and user-friendly rating. To ensure that quality is maintained when using the afore mentioned model one must have the foundation of suitable yield curves. In May 2002 a further reassessment of PEI yield curves began with the inclusion of the most recent PSP volume and age data. This report explains the method involved in the examination of the data for plantation grown species. The result of the assessment and the discussion are shown. Additionally the assessment of Norway spruce Picea abies (L) Karsten and Japanese and European larch Larix decidua Miller, Larix kaempferi (Lamb.) Carriere included a comparison with cumulative volume to age data of specific Yield Classes (YC) from the British Forestry Commission’s Yield Models for Forest Management. The report details the interpretation of these comparisons.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".