Integrating herbarium data with spatial biodiversity assessment into conservation plans: A case study of the genus Carex L. (Cyperaceae) in Saskatchewan, Canada
Bibliographic record
Abstract
Sedge (Carex L.) is the largest genus of vascular plants in Saskatchewan, Canada, where it is represented by 105 species. The aim of this study was to develop an effective procedure to assess the conservation status of sedges in the province. Data on 49 target Carex species were collected, validated, and consolidated from the Flora of Saskatchewan Association (FOSA) and the Global Biodiversity Information Facility (GBIF) datasets, resulting in 277 specimen-based occurrences. Applying a novel assessment approach, target Carex species were classified as follows: Critically Endangered (CR) – six taxa, Endangered (EN) – four, Vulnerable (VU) – eight, Near Threatened (NT) – three, Least Concern (LC) – 24, and Data Deficient (DD) – four. This allowed for a substantial reduction to a list of rare sedges compiled by the Saskatchewan Conservation Data Centre (SKCDC) from 40 species (38.1% in the genus) to 21 species (20.0%). In terms of territorial protection, rare sedges (CR, EN, VU and NT conservation categories) have been recorded only in 13 or 4.0% of protected areas in Saskatchewan. Most sedges (12 species or 57.1%) have no recorded occurrences in the protected areas. This group is followed by five species (23.8%) found in a single protected area, three species (14.3%) recorded in two protected areas, and one species (4.76%) observed in three protected areas. Anthropogenic land use changes such as agriculture, urbanization, and industrial activities are the major threats to sedges in Saskatchewan. An effective action plan for the conservation of Carex species is crucial to reduce threats to this group of plants. Our results provide a scientific basis for the long-term conservation of sedge diversity in Saskatchewan.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".