A multivariate study of moss distributions in relation to environment in the Gulf of St. Lawrence region, Canada
Bibliographic record
Abstract
Moss distribution patterns in the Gulf of St. Lawrence were investigated using multivariate analyses to determine the relationship of the patterns to environmental factors. Distance-based redundancy analysis was used to ordinate 29 operational geographical units (OGU) or sampling units based on their moss floras, and hierarchical cluster analysis in combination with indicator analysis was used to produce classifications of both species and sampling units. Climatic variables, in particular, warmth of the growing season, were the most important factors determining species distribution; this resulted in a northsouth gradient through the study area. Oceanity was also shown to be important and manifested as an eastwest gradient. Edaphic factors, in particular, amount of calcareous rock outcrop, had a secondary influence and modified the patterns established by climate. Ordination of OGUs showed the effects of environment to be more variable in the northern half of the Gulf of St. Lawrence, which may in part explain the higher species richness there. Seven OGU groups were recognized based on cluster analysis of floristic composition. Although indicator species were few, most groups were distinguished by unique sets of regionally rare species. Eleven species elements were identified based on species occurrence in OGUs. The elements constituted sets of overlapping distributions showing southern, northern, and eastern biases in the Gulf region. Multivariate analysis was shown to be effective tool for extracting mossenvironment patterns, even at medium geographic scale.Key words: Gulf of St. Lawrence, mosses, environment, richness, distribution, ordination, cluster analysis.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".