Bibliographic record
Abstract
This study attempts to introduce elements of data uncertainty into the Shore and Safranyik model for approximating risk and susceptibility ratings for Mountain Pine Beetle attack in the Morice Forest District of British Columbia, Canada. Data uncertainty is introduced into the modeling procedure by introducing attribute uncertainty and spatial (boundary) vagueness into representations of Forest Inventory Polygon data, an essential component of the Shore and Safranyik modeling system, for the purposes of assessing the effects of uncertainty on the results of the Shore and Safranyik susceptibility modeling procedure. Introducing uncertainty into the forest inventory data is done by introducing spatial vagueness to the discrete forest polygon data (accomplished using a geostatistical approach) as well as attribute uncertainty through a neighbourhood-based weighting algorithm, resulting in a forest inventory representation which incorporates both spatial and attribute uncertainty and can be incorporated into the Shore and Safranyik modeling procedure. The impacts of data uncertainty are assessed by comparing the results of the Shore and Safranyik model derived using representations of forest data which incorporates uncertainty, versus the use of traditional or discrete data inputs.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".