Contemporary Issues in Québec’s Temperate Forest — Part 2: Biological Invasions
Bibliographic record
Abstract
This paper is the second in a series on the topic of contemporary issues in Québec’s temperate forest. It considers biological invasions that may either cause new and significant mortality among indigenous trees or substantially alter those species’ regeneration processes in the forest. Our review of government authority websites and scientific literature led us to identify 11 species that are vulnerable or highly vulnerable to exotic or emergent pests, 14 that are less vulnerable and 11 in an intermediate situation. The most vulnerable species do not include Québec’s three most abundant temperate hardwood species, namely sugar maple ( Acer saccharum Marsh.), red maple ( Acer rubrum L.) and yellow birch ( Betula alleghaniensis Britt.). They do, however, include certain maple forest companion species. We also identified three animal groups, two tree species, three shrub species and five herbaceous species groups that, if they were to invade the forest, could have significant consequences for entire stands as opposed to specific tree species, by disturbing the undergrowth. Invasions such as these enhance the risk of losing biodiversity and forest productivity, thereby making productivity less predictable and creating challenges for assisted tree migration initiatives. On the other hand, they offer a potential opportunity for mitigating the invasiveness of certain other species such as the American beech ( Fagus grandifolia Ehrh.).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".