Habitat loss exceeds habitat regeneration for an IUCN flagship lichen epiphyte: <i>Erioderma pedicellatum</i>
Bibliographic record
Abstract
The boreal felt lichen (Erioderma pedicellatum (Hue) P.M. Jørg.) is globally critically endangered, being threatened by forestry operations, habitat disturbance, and air pollution. To determine if loss of habitat due to forestry activities has occurred in Nova Scotia, a predictive habitat model was built using historical data from 1988. Satellite data were used for the period between 1987 and 2005 to determine the amount of suitable habitat harvested during this period. Available habitat was modeled through time from 1988 to 2005 in which area harvested was subtracted and regeneration was added in 3- to 5-year time steps. The predicted suitable boreal felt lichen habitat area was then modeled from 2005 to 2055 using the same harvesting assumptions and modeling process, but using 10-year time steps. The results of the model indicated that there has been a loss of 2311 ha (11.5%) in the amount of predicted suitable boreal felt lichen habitat between 1988 and 2005. A forward-projected drop is predicted between 2005 and 2055 that will amount to 4499 ha (25.4%), assuming no change in forest harvesting. Protection of unoccupied habitat surrounding existing boreal felt is recommended.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".