Epigeous macrofungal succession in the first five years following a wildfire in karri (<i>Eucalyptus diversicolor</i>) regrowth forest in Western Australia
Bibliographic record
Abstract
Abstract Following a wildfire in 17–25‐year‐old regrowth karri ( Eucalyptus diversicolor ) forest in the southwest of Western Australia, plots were established in burnt and similarly aged unburnt forest to monitor the fruiting of macrofungi. Thirty‐six plots on 10 sites (five burnt, five unburnt) were surveyed over a 5‐year period. Plots were surveyed every 2 weeks in the macrofungal fruiting season (April to October) and monthly for the remainder of each year. A total of 332 species were recorded. Fire did not impact significantly on mean species richness. However, a distinct mycoflora was recorded on burnt sites, and species composition on burnt sites changed substantially for each year following the fire and after 5 years was still different from that on unburnt sites. Nineteen percent of species recorded were regarded as being present as a direct result of the fire. The study also demonstrated the variable nature of macrofungal sporophore production in the absence of disturbance and the importance of regular sampling. Five distinct succession groups of post‐fire fungi were recognized. The adaptive traits of post‐fire fungi in relation to fire and the management of fire for macrofungal diversity are discussed.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".