Macrofungal conservation in Canada and target species for assessment: a starting point
Bibliographic record
Abstract
Despite the ecological importance of fungi, we still know little about their diversity in Canada. One of the largest hurdles to implementing fungal conservation initiatives is the lack of fungal distribution data. As anthropogenic impacts accelerate the speed of environmental change, it is imperative that we fill this major information gap, critical for fungal protection. To gain insight on the conservation status of Canadian macrofungi, we took advantage of the large and growing body of fungal biodiversity data from government research ( Wild Species 2020), citizen science, trained independent mycologists, university, and museum biodiversity research. The majority of macrofungi are data deficient; we do not know their geographic distribution or habitat requirements, occurrence, or abundance in Canada. For mushrooms that fruit only a few days of the year and are often difficult to positively identify, there is a lot of work to overcome the uncertainty of distinguishing under-sampling from rarity. Our work stresses the importance of building a strong network of professional and amateur mycologists to develop resources, disseminate information to make educated decisions, and advance conservation actions. We found that several fungi can be prioritized; we present a short list for consideration for formal conservation assessment.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".