Factors influencing the distributions of two endangered lichens in Nova Scotia, Canada
Bibliographic record
Abstract
The lichens Erioderma pedicellatum (Hue) P.M. Jørg. and Erioderma mollissimum (G. Sampaio) Du Rietz are endangered species in Canada. Both species are obligate epiphytes found in forested wetlands near the Atlantic Coast. They are thought to be primarily threatened by logging and acid pollution, but the influence of these factors has not been examined at large-extents or relative to other habitat features. Critical habitat for protection has remained difficult to define beyond observed occurrences, because of the low accuracy of existing habitat models. To facilitate improved recovery planning and understanding of their ecology in the province of Nova Scotia, we created high-resolution distribution models for both species, incorporating elements of climate, forest composition, hydrology, acid pollution, and anthropogenic influence using the MaxEnt algorithm and a backwards stepwise selection process. The most important predictors were related to rainfall or an oceanic moderation of thermal optima. Depth to water table and the presence of suitable forest composition were also included, as was distance from roads for E. pedicellatum. The putative threats, acid pollution and silvicultural treatment, were not important and therefore excluded from models. Although both species have highly specific habitat requirements, E. pedicellatum appears to be more sensitive to human activities.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".