Macrofungal diversity and habitat specificity: a case study
Bibliographic record
Abstract
The diversity of macrofungi in the lateritic region of West Bengal was explored and 120 species, one subgenus and a variety, having eight ecological functions, were found to grow among three types of habitats, i.e. natural forests, plantation forests and villages. Yate’s corrected chi-square (χ2) test statistic was performed upon the 2 × 2 table (contingency table) and testing the null hypotheses of independence of observed cell frequencies of the presence/absence of a species in a given habitat type. Various degrees of specificities of macrofungi to their habitats were observed, i.e. Amanita vaginata, Astraeus hygrometricus, Laccaria laccata, Lactarius zonarius, Porphyrellus malaccensis, Russula brevipes, Russula delica, Russula emetica and Russula laurocerasi were absolutely specific for natural forests; Pisolithus arhizus and Ramaria fumigata were absolutely specific for plantation forests; Auricularia auricula, Schizophyllum commune and Termitomyces clypeatus (only association coefficient 100%) were found to absolutely specific for village habitat. MS Excel-based formulas for calculation of association/specificity of species to habitat and species to species as well as other diversity indices are provided. Local and tribal populations used 19 species of macrofungi during their fruiting period, of which 17 had culinary values and four were locally considered medicinal. This study is a first of its kind, and has various applications to allied disciplines in understanding diversity, ecology and biological prospects of the macrofungal realm.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".