Bibliographic record
Abstract
Canadian forests are naturally dynamic ecosystems, changing species composition and abundance as the ecosystem evolves through succession or reacts to disturbances, such as wind and insects. Pollution and climate change will be the largest stressors to Canada's forests in the future. Their future impact on the health of Canada's forests remains speculative. Lichens have been identified as valuable indicators of forest health; however, there are no comprehensive datasets on which lichens are indicative of forest health in Canada. An analysis of the existing literature reveals a large number of lichens that can be used to monitor levels of various pollutants (general pollution: 51 species; sulphur dioxide: 42 species; photochemicals: 23 species; fluoride: 18 species; heavy metals: 3 species; acid precipitation: 8 species; sulphite: 2 species; nitrate: 2 species). The use of lichens as indicators of climate change is also reported in the literature but, there are insufficient data to monitor the effects of climate change on lichen communities in North America. While various provincial and federal government departments and industries have been monitoring lichen communities across Canada for up to nearly three decades, there exists no standard monitoring protocol for lichens in Canada, which makes comparisons among studies challenging. The development of a standard monitoring protocol would allow integration of the various initiatives into a nationwide lichen monitoring program. Key words: lichens, biomonitoring, forest health, pollution, climate change
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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".