Impact of climate change on the distribution of Isaria cicadaeMiquel in China: predictions based on the MaxEnt model
Bibliographic record
Abstract
Introduction Isaria cicadae , a historically valued edible and medicinal fungus in China, has been experiencing a critical decline in abundance due to ecological degradation and overexploitation. Understanding its potential distribution is essential for promoting sustainable harvesting practices. Methods This study utilizes the MaxEnt model, combined with known distribution records and 22 environmental variables, to predict the potential distribution of I. cicadae under three representative emission scenarios (CMIP6: SSP1-2.6, SSP2-4.5, and SSP5-8.5) for the 2050s and 2070s. Results The analysis identifies seven key environmental variables influencing the habitat suitability of I. cicadae : the mean temperature of the driest quarter (bio09), the mean temperature of the wettest quarter (bio08), precipitation in the wettest month (bio16), the mean diurnal range (bio02), isothermality (bio03), elevation, and slope. Currently, I. cicadae is mainly found in the provinces of Yunnan, Sichuan, Hunan, Hubei, Guizhou, Jiangxi, Guangxi, Fujian, Anhui, and Zhejiang, with Yunnan and Sichuan having the largest areas of high suitability at 25.79 × 10 4 km 2 and 21.36 × 10 4 km 2 , respectively. Discussion Jiangxi, Hunan, Yunnan, Guizhou, Fujian, and the Guangxi Zhuang Autonomous Region are identified as primary regions of high suitability. This study aims to further elucidate the impact of the environment on the distribution of I. cicadae from a geographical perspective and provide theoretical insights for the future cultivation and conservation strategies of this species.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".