Bibliographic record
Abstract
Insects and diseases exert immense economic loss to forests through reduced tree growth and mortality. In recent years, these problems have been exacerbated by climate change. For example, in British Columbia, warmer summers and an amelioration of winter temperatures have allowed the mountain pine beetle to expand its range into new areas of British Columbia and Alberta east of the Rocky Mountains. A significant concern is subsequent expansion of this insect into jack pine within the boreal forest. Another concern, as yet unstudied, is the potential expansion of mountain pine beetle northward through lodgepole pine into northern British Columbia and the Yukon. Range expansion and/or increased tree mortality is not limited to mountain pine beetle. Indeed, many other insects such as Douglas fir beetle, Western balsam bark beetle, and spruce beetle have reached record population levels in British Columbia in recent years. Such population dynamics are likely due to a phenomenon known as the Moran effect, in which populations with the same density-dependent structure erupt simultaneously when synchronized by a landscape-level exogenous variable. The most plausible exogenous variable in forest systems is temperature, especially for uni-or semi-voltine insects dependent upon phenological synchrony. A changing climate, especially increasing temperatures, may not only extend ranges but also bring about simultaneous populations eruptions. There has been a plethora of work on models examining population dynamics of the mountain pine beetle. Predominant approaches have included deterministic process models with relative risk, simulation outputs (Riel et al. 2004), analytical process models designed to mirror system behaviour
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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.895 | 0.705 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".