Arboreal macrolichen community composition and habitat associations in boreal forested wetlands of Newfoundland, Canada
Bibliographic record
Abstract
Forested wetlands are ecologically and economically important, but many are poorly understood. A robust inventory of species is important for sound management in these ecosystems, particularly ones that include cryptogams such as arboreal lichens, which are rich and abundant in forested wetlands. On the island of Newfoundland, Canada, little is known about what lichens are found in forested wetlands, how lichen communities interact with different forested wetlands, or whether there are lichens unique to forested wetlands. Therefore, we investigated the potential for macrolichens to act as indicators of forested bog, fen and swamp wetland classes in four regions. We counted macrolichen thalli, by species, on the lower bole of black spruce (Picea mariana) trees within plots from each forested wetland class in each region. We also collected data on habitat characteristics in each wetland: soil pH, canopy closure, and ground and shrub cover, all of which differed significantly among forested wetland classes. Macrolichen communities differed among regions and forested wetland classes but the greatest differences were among regions. We also attempted to identify reliable macrolichen indicator species for forested wetland classes and regions but were unsuccessful. A lichen of conservation concern, Erioderma pedicellatum (Hue) P.M.Jørg., was detected in some of our forested wetland sites, highlighting the importance of proper management of these unique habitats.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".