Evaluation of Risk Assessment of Mountain Pine Beetle Infestations
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 Decision support systems to aid the management of mountain pine beetles combine characteristics of the stand and beetle infestation to estimate risk of damage. Beetle infestation information is now available in a format amenable to the operational implementation of risk. In this study, an established risk rating system was evaluated to determine the utility of the values generated. For a study area located in British Columbia, Canada, global positioning systems were used to survey an infestation. The annual data was used to generate risk for a given year and to compare the ratings with survey data from the subsequent year. Under epidemic conditions, 30% to 43% of the stands rated as high risk were subsequently infested. Of the infested stands, 72% to 76% had a high risk rating. In general, the risk rating system accurately predicted risk in stands that were infested, but not all high risk stands were subsequently attacked. This highlights the difficulty of modeling processes that have a stochastic component. For operational contexts, the estimation of risk on an annual basis is sufficiently reliable to aid in the strategic planning of forest managers.
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.
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 | 0.001 | 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) | 0.000 | 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