A compendium of macrofungi of Pakistan by ecoregions
Bibliographic record
Abstract
Macrofungi form fruiting bodies that can be detected with the naked eye in the field and handled by hand. They mostly consist of basidiomycetes, but also include some ascomycetes. Mycology in Pakistan is still in its infancy, but there have been many historical reports and checklists of macrofungi occurrence from its 15 ecoregions, which range from Himalayan alpine grasslands and subtropical pine forests to deserts and xeric shrublands. In this work, we searched and reviewed the historical literature and the GenBank database for compiling a comprehensive list of macrofungi reported from Pakistan to date. We recorded 1,293 species belonging to 411 genera, 115 families and 24 orders. These occurrences were updated taxonomically following the classification system currently proposed in the Index Fungorum website. The highest represented order by taxon number is Agaricales (47%) with 31 families, 146 genera and 602 species, followed by Polyporales (11%), Russulales (9%) and Pezizales (8%). Genera occurrence reported therein are presented for each ecoregion to the best of our ability given the data. We also discussed the currently known macrofungi diversity between different ecoregions in Pakistan. Overall, this work should serve as a solid foundation for the inclusion of Pakistan macrofungi in global biodiversity and conservation studies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".