<i>Auricularia auricula-judae</i> . [Descriptions of Fungi and Bacteria].
Bibliographic record
Abstract
Abstract A description is provided for Auricularia auricula-judae , found on dead branches of Sambucus nigra . Some information on its morphology, dispersal and transmission and conservation status is given, along with details of its geographical distribution (Africa (Benin, Côte d'Ivoire, Congo, Democratic Republic of the Congo, Ghana, Ethiopia, Gabon, Kenya, Madagascar, Malawi, Nigeria, Rwanda, São Tomé and Principe, Sierra Leone, South Africa, Tunisia, Uganda, Zambia), Asia (Armenia, Azerbaijan, Bhutan, Cambodia, China, Fujian, Hainan, Hong Kong, Manchuria, Shaanxi, Yunnan, Cyprus, Republic of Georgia, India, Assam, Chhattisgarh, Himachal Pradesh, Jammu and Kashmir, Karnataka, Kerala, Maharashtra, Manipur, Meghalaya, Nagaland, Rajasthan, Sikkim, Uttarakhand, West Bengal, Indonesia, Iran, Israel, Japan, Laos, Lebanon, Malaysia, North Korea, Pakistan, Palestine, Papua New Guinea, Philippines, Russia, Altai Republic, Amur Oblast, Jewish Autonomous Oblast, Khabarovsk Krai, Primorsky Krai, Republic of Sakha, Sakhalin Oblast,, Singapore, Korea Republic, Sri Lanka, Taiwan, Thailand, Turkey, Vietnam), Atlantic Ocean (Portugal, Madeira, Spain, Islas Canarias), Australasia (Australia, New South Wales, Northern Territory, Queensland, Victoria, New Zealand), Caribbean (American Virgin Islands, Cuba, Haiti, Jamaica, Puerto Rico), Central America (Costa Rica, El Salvador, Guatemala, Honduras, Panama), Europe (Andorra, Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Irish Republic, Isle of Man, Italy, Jersey, Liechtenstein, Lithuania, Luxembourg, Malta, Moldova, Netherlands, Norway, Poland, Portugal, Romania, Russia, Belgorod Oblast, Karachay-Cherkess Republic, Krasnodar Krai, Kursk Oblast, Leningrad Oblast, Moscow Oblast, Republic of Adygea, Republic of North Ossetia-Alania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Ukraine, UK), Indian Ocean (Mauritius), North America (Canada, Alberta, British Columbia, New Brunswick, Nova Scotia, Ontario, Prince Edward Island, Quebec, Mexico, USA, Alabama, Alaska, Arizona, Arkansas, California, Colorado, Connecticut, Florida, Georgia, Idaho, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, Maryland, Michigan, Minnesota, Mississippi, Missouri, Montana, New Hampshire, New Jersey, New Mexico, New York, North Carolina, Ohio, Oregon, Pennsylvania, South Carolina, Tennessee, Texas, Utah, Vermont, Virginia, Washington, West Virginia, Wisconsin, Wyoming), Pacific Ocean (French Polynesia, Guam, Norfolk Island, USA, Hawaii), South America (Argentina, Bolivia, Brazil, Amazonas, Bahia, Espírito Santo, Mato Grosso, Pará, Paraná, Pernambuco, Rio de Janeiro, Rio Grande do Norte, Rio Grande do Sul, Rondônia, Roraima, Santa Catarina, São Paulo, Chile, Colombia, Ecuador, French Guiana, Guyana, Paraguay, Peru, Suriname, Venezuela)) and host ( S. nigra ).
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.011 |
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".