Mi'kmaw knowledge helps uncover a new area of interesting lichen biodiversity on the island of Newfoundland (Ktaqmkuk)
Bibliographic record
Abstract
The island of Newfoundland, Canada, is known as an area with high lichen species richness; however, most of this diversity is known from coastal regions where the ocean creates a maritime climate. The central part of the island has a more continental climate and is also the part of the province with the highest levels of industrial forest harvest and mining activities. For these reasons, it has not been an area considered to have high lichen diversity. Here, we show how local Mi'kmaw knowledge in collaboration with western scientific expertise facilitated a two-eyed seeing approach (Etuaptmumk) that yielded the discovery of overlooked lichen diversity in Central Newfoundland. Surveys by the authors throughout 2023 yielded collections of 175 species of lichenized, lichenicolous and allied fungi from the area known as Charlie's Place. Of these, there is a high proportion of cyanolichens (13%) and calicioids (11%), indicating high ecological value and potential old growth/ancient forest status. In addition, we report 19 new species records for the province, two of which (Chaenothecopsis vainioana and Myrionora albidula) are new records for Canada. Overall, the survey work reported here suggests that Charlie's Place should be a priority area for protection within the context of Central Newfoundland. This work also illustrates the value of research under the framework of Etuaptmumk and the benefits of combining local Indigenous and western scientific knowledge. The political, logistical, and financial support of Qalipu First Nation was key to the success of this work.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".