COVID-19 and Forests in Canada and the United States: Initial Assessment and Beyond
Bibliographic record
Abstract
Information on the initial effects of a novel coronavirus, COVID-19, during 2020 on forests in Canada and the United States was derived from existing published studies and reports, news items, and policy briefs, amplified by information from interviews with key informants. Actions taken by governments and individuals to control the spread of the virus and mitigate economic impacts caused short-term disruptions in forest products supply chains and accelerated recent trends in consumer behavior. The COVID-19 containment measures delayed or postponed forest management and research; a surge in visitation of forests near urban areas increased vandalism, garbage accumulation, and the danger of fire ignitions. Forests and parks in remote rural areas experienced lower use, particularly those favored by international visitors, negatively affecting nearby communities dependent upon tourism. Physical distancing and isolation increased on-line shopping, remote working and learning; rather than emerging as novel drivers of change, these actions largely accelerated existing trends. On-line shopping sales had a positive effect on the packaging sector and remote working had a negative effect on graphic paper manufacturing. More time at home and low interest rates increased home construction and remodeling, causing historically high lumber prices and localized material shortages. The response to the pandemic has shown that rapid social change is possible; COVID-19 presents a once in-a-lifetime opportunity to shift the global development paradigm toward greater sustainability and a greener, more inclusive economy, in which forests can play a key role. In both Canada and the United States, the notion of directing stimulus and recovery spending beyond meeting immediate needs toward targeting infrastructure development has momentum.
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 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".