Patterns of rare moss diversity and distribution in Alberta
Bibliographic record
Abstract
Rare moss diversity and distribution patterns are described for the province of Alberta. Multivariate analyses were used to elucidate the distribution patterns of the rare mosses in relation to environmental factors, in comparison with those of the common mosses. Hierarchical clustering of species occurrence among Alberta ecoregions resulted in 11 species groups having similar distributions and four ecoregion groups with similar species composition. Rare and common mosses show comparable distributions at the geographic scale of ecoregions; the analyses did not provide support that rare mosses respond to environment factors differently than common mosses. The rare mosses are more narrowly distributed among the species and ecoregion groups than are common species. The most important environmental variables determining species distributions are mean annual temperature and growing degree days. Other important factors determining moss distribution are mean elevation, annual rainfall, and mountain landscape. Diversity patterns are shown to vary widely among the ecoregions and to differ between rare and common species. Rare species are especially diverse in the Rocky Mountains, which support 81% of the province’s known rare moss flora and reflect a complex of diverse landscapes for moss colonization. Only 40% of known rare species in Alberta are found in nonmountainous regions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".